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The following time series has an anomaly with short term disruption on its pattern. What is the likely pattern of the time series without the anomaly? | [
"Sine wave with linear trend",
"Sawtooth wave with linear trend",
"Square wave with log trend"
] | Square wave with log trend | multiple_choice | 73 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Sawtooth Wave",
"Square Wave",
"Linear Trend",
"Log Trend",
"Wander Anomaly"
] | Wander anomaly brings short term disruption on the pattern. You should focus on the overall pattern of the time series without the anomaly. | Anolmaly Detection | General Anomaly Detection | 201 | [
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] | null |
What type of noise is present in the given time series? | [
"Gaussian White Noise",
"No significant noise",
"Red Noise"
] | Gaussian White Noise | multiple_choice | 63 | medium | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Gaussian White Noise",
"Red Noise"
] | Observe the pattern of fluctuations in the time series. | Noise Understanding | Signal to Noise Ratio Understanding | 202 | [
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] | null |
The given time series has a cyclic component and a trend component added together. What is the most likely type of the trend component? | [
"Exponential",
"Linear",
"Log"
] | Exponential | multiple_choice | 10 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Linear Trend",
"Exponential Trend",
"Log Trend",
"Sine Wave",
"Additive Composition"
] | Despite having a cyclic component, check the general trend of the time series. | Pattern Recognition | Trend Recognition | 203 | [
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] | null |
The given time series is a white noise process. What is the most likely noise level? | [
"3.94",
"7.08",
"1.05"
] | 7.08 | multiple_choice | 51 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Gaussian White Noise"
] | The noise level refers to the standard deviation of the noise. You should check the degree of variation of the time series over time. You can estimate the standard deviation by observing the average distance between the data points and the mean. | Noise Understanding | White Noise Recognition | 204 | [
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] | null |
What is the primary cyclic pattern observed in the time series? | [
"SawtoothWave",
"No Pattern at all",
"SquareWave",
"SineWave"
] | SawtoothWave | multiple-choice | 15 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Square Wave",
"Sawtooth Wave"
] | Check the overall shape of the time series against the definition of provided concepts | Pattern Recognition | Cycle Recognition | 205 | [
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] | null |
What is the most dominant pattern in this complex time series? | [
"Trend",
"Noise",
"Seasonality"
] | Trend | multiple_choice | 13 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Linear Trend",
"Sine Wave",
"Gaussian White Noise"
] | Identify which component (trend, seasonality, or noise) has the largest impact on the overall pattern. | Pattern Recognition | Trend Recognition | 206 | [
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] | null |
Two time series are given. Both of them have a noise component. Do they have the same type of noise? | [
"Yes, they both have Gaussian white noise",
"No, they have different noise"
] | Yes, they both have Gaussian white noise | binary | 87 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Gaussian White Noise",
"Red Noise",
"Additive Composition"
] | When a white noise is added to a time series, it is expected the random fluctuations have similar amplitude or distribution. Random walk, on the other hand, can result in very different noise patterns. | Similarity Analysis | Shape | 207 | [
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] |
Is the given time series likely to have a non-stationary anomaly? | [
"Yes, due to trend reversal",
"Yes, due to cutoff",
"No, the anomaly is stationary"
] | Yes, due to trend reversal | binary | 70 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Stationarity",
"Linear Trend",
"Sine Wave",
"Cutoff Anomaly",
"Spike Anomaly"
] | Non-stationary anomaly refers to the anomaly that changes over time. You should check if the time series has a constant mean and variance over time. If not, you should check the type of anomaly based on the given definitions. For example, spikes anomaly are stationary. | Anolmaly Detection | General Anomaly Detection | 208 | [
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] | null |
Is the given time series likely to have an anomaly? | [
"Yes, it's pattern is distorted by random spikes",
"No",
"Yes, it's pattern is flipped at certain point in time"
] | Yes, it's pattern is distorted by random spikes | binary | 63 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Flip Anomaly",
"Spike Anomaly"
] | Anomaly is an observation that deviates from the general pattern in the time series. You should check if the time series has any sudden changes or unexpected patterns. If so, check the type of anomaly based on the given definitions. | Anolmaly Detection | General Anomaly Detection | 209 | [
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] | null |
What is the type of the trend of the given time series? | [
"Exponential",
"No Trend",
"Linear"
] | No Trend | multiple_choice | 1 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Linear Trend",
"Exponential Trend"
] | It would be helpful to check if slope of the time series changes over time. | Pattern Recognition | Trend Recognition | 210 | [
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] | null |
The given time series is a sine wave. What is the most likely amplitude of the sine wave? | [
"8.61",
"5.85",
"1.44"
] | 8.61 | multiple-choice | 22 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Amplitude"
] | Check the distance between the peak and the baseline. | Pattern Recognition | Cycle Recognition | 211 | [
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] | null |
Is the noise in the time series more likely to be additive or multiplicative to the signal? | [
"Multiplicative",
"Additive"
] | Multiplicative | binary | 57 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Additive Composition",
"Multiplicative Composition",
"Gaussian White Noise"
] | Additive noise is added to the signal, while multiplicative noise is multiplied to the signal. When a trend component is added with a white noise, the general trend still remains. When a trend component is multiplied with a white noise, the noise is amplified. Can you check if it is the case for the given time series? | Noise Understanding | Signal to Noise Ratio Understanding | 212 | [
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] | null |
Does any part of the given time series, composed of several concatenated patterns, appear to be stationary? | [
"No",
"Yes"
] | Yes | binary | 32 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Stationarity"
] | You can try to identify different parts in the time series first, and see if any part is stationary. | Pattern Recognition | Stationarity Detection | 213 | [
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] | null |
What is the most likely linear trend coefficient of the given time series? | [
"3.78",
"0",
"8.1"
] | 8.1 | multiple_choice | 2 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Linear Trend"
] | The bigger the slope of the line, the higher the trend coefficient. | Pattern Recognition | Trend Recognition | 214 | [
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] | null |
Despite the noise, does the given two time series have similar pattern? | [
"Yes, they have similar shape",
"No, they have different shape"
] | No, they have different shape | binary | 80 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Square Wave"
] | Noise refers to the random fluctuations in the time series. You should focus on the overall pattern of the time series. Pattern refers to the general shape of the time series. In this case, you see both time series have cyclic patterns. Do their behaviors at peak and trough look similar? | Similarity Analysis | Shape | 215 | [
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] |
Is the given time series stationary? | [
"Yes",
"No"
] | No | binary | 30 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Stationarity"
] | Try to see if the time series has a constant mean, and degree of variation over time. | Pattern Recognition | Stationarity Detection | 216 | [
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] | null |
Is the given time series stationary? | [
"Yes",
"No"
] | No | binary | 30 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Stationarity"
] | Try to see if the time series has a constant mean, and degree of variation over time. | Pattern Recognition | Stationarity Detection | 217 | [
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] | null |
You are given two time series which both have trend components. Do they have the same type of trend? | [
"Yes, they both have exponential trend",
"No, time series 1 has linear trend and time series 2 has exponential trend",
"No, time series 1 has exponential trend and time series 2 has log trend"
] | Yes, they both have exponential trend | multiple_choice | 85 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Linear Trend",
"Exponential Trend",
"Log Trend"
] | First identify the trend component for each time series. Then, check if they are equal. | Similarity Analysis | Shape | 218 | [
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] |
You are given two time series following similar pattern. Both of them have an anomaly. What is the likely type of anomaly in each time series? | [
"Time series 1 with cutoff anomaly and time series 2 with speed up/down anomaly",
"Time series 1 with flip anomaly and time series 2 with speed up/down anomaly",
"Time series 1 with cutoff anomaly and time series 2 with flip anomaly"
] | Time series 1 with flip anomaly and time series 2 with speed up/down anomaly | multiple_choice | 74 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Sawtooth Wave",
"Linear Trend",
"Log Trend",
"Cutoff Anomaly",
"Flip Anomaly",
"Speed Up/Down Anomaly"
] | You already know both time series have an anomaly. You should treat them separately and check the type of anomaly based on the given definitions. | Anolmaly Detection | General Anomaly Detection | 219 | [
0,
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] | [
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] |
Based on the given time series, how many different regimes are there? | [
"1",
"4",
"3"
] | 3 | multiple_choice | 40 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Regime Switching"
] | First identify the different patterns in the time series. It might be helpful to identify their individual starting and ending points. Then, count the number of different patterns. | Pattern Recognition | Regime Switching Detection | 220 | [
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] | null |
Are the two time series flipped versions of each other despite noise? | [
"Yes, they are flipped versions",
"No, they are not flipped versions"
] | No, they are not flipped versions | binary | 91 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Linear Trend"
] | Both time series have a trend and a cyclic component. Then we say two time series are flipped versions of each other, we mean that the sign of each step is flipped. You should check if the sign of each step is flipped for both time series. At a high level, you should check if the time series are mirror images of each other. | Similarity Analysis | Shape | 221 | [
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] |
In which part of the time series does the anomaly occur? | [
"Beginning",
"Middle",
"End"
] | Beginning | multiple_choice | 77 | medium | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Linear Trend",
"Spike Anomaly",
"Cutoff Anomaly",
"Wander Anomaly"
] | Identify where in the time series sequence the unusual pattern or disruption occurs. | Anolmaly Detection | General Anomaly Detection | 222 | [
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] | null |
You are given two time series following similar pattern. One has an anomaly and the other does not. Which time series has the anomaly, and what is the likely type of anomaly? | [
"Time series 1 with flip anomaly",
"Time series 1 with speed up/down anomaly",
"Time series 2 with cutoff anomaly"
] | Time series 1 with speed up/down anomaly | multiple_choice | 74 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Linear Trend",
"Speed Up/Down Anomaly",
"Cutoff Anomaly",
"Flip Anomaly"
] | You should first identify the time series with the anomaly. Remember, both time series share similar pattern. Then, you should check the type of anomaly based on the given definitions. | Anolmaly Detection | General Anomaly Detection | 223 | [
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] |
Is the given time series likely to be stationary after removing the trend? | [
"No",
"Yes"
] | No | binary | 35 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Stationarity",
"Linear Trend",
"Exponential Trend"
] | Trend brings the overall shape of the time series up or down. Assume this effect is removed, does the time series satisfy the stationarity condition? | Pattern Recognition | Stationarity Detection | 224 | [
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] | null |
You are given two time series which both have trend components. Do they have the same type of trend? | [
"No, time series 1 has linear trend and time series 2 has exponential trend",
"No, time series 1 has exponential trend and time series 2 has log trend",
"Yes, they both have exponential trend"
] | Yes, they both have exponential trend | multiple_choice | 86 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Linear Trend",
"Exponential Trend",
"Log Trend"
] | First identify the trend component for each time series. Then, check if they are equal. | Similarity Analysis | Shape | 225 | [
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] |
You are given two time series following similar pattern. One has an anomaly and the other does not. Which time series has the anomaly, and what is the likely type of anomaly? | [
"Time series 1 with speed up/down anomaly",
"Time series 1 with flip anomaly",
"Time series 2 with cutoff anomaly"
] | Time series 2 with cutoff anomaly | multiple_choice | 74 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Linear Trend",
"Speed Up/Down Anomaly",
"Cutoff Anomaly",
"Flip Anomaly"
] | You should first identify the time series with the anomaly. Remember, both time series share similar pattern. Then, you should check the type of anomaly based on the given definitions. | Anolmaly Detection | General Anomaly Detection | 226 | [
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] |
The given time series is a random walk process. What is the most likely noise level? | [
"1.13",
"4.36",
"6.83"
] | 4.36 | multiple_choice | 54 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Red Noise"
] | The noise level refers to the standard deviation of the noise. You should check the degree of variation of the time series over time. You can estimate the standard deviation by observing the average distance between the data points and the past value. | Noise Understanding | Red Noise Recognition | 227 | [
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] | null |
You are given two time series following similar pattern. Both of them have an anomaly. What is the likely type of anomaly in each time series? | [
"Time series 1 with cutoff anomaly and time series 2 with flip anomaly",
"Time series 1 with flip anomaly and time series 2 with speed up/down anomaly",
"Time series 1 with cutoff anomaly and time series 2 with speed up/down anomaly"
] | Time series 1 with cutoff anomaly and time series 2 with speed up/down anomaly | multiple_choice | 74 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Sawtooth Wave",
"Linear Trend",
"Log Trend",
"Cutoff Anomaly",
"Flip Anomaly",
"Speed Up/Down Anomaly"
] | You already know both time series have an anomaly. You should treat them separately and check the type of anomaly based on the given definitions. | Anolmaly Detection | General Anomaly Detection | 228 | [
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You are given two time series with same underlying pattern but different noise level. Which time series has higher magnitude of noise? | [
"Time series 1",
"Time series 2"
] | Time series 1 | multiple_choice | 60 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Exponential Trend",
"Gaussian White Noise",
"Variance"
] | When the noise level is high, it can distort the pattern in the time series. Both time series have the same underlying pattern, but different noise level. To tell which time series has higher noise level, you should check the degree of distortion of the time series pattern. | Noise Understanding | Signal to Noise Ratio Understanding | 229 | [
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] |
You are given two time series following similar pattern. One has an anomaly and the other does not. Which time series has the anomaly, and what is the likely type of anomaly? | [
"Time series 2 with cutoff anomaly",
"Time series 1 with flip anomaly",
"Time series 1 with speed up/down anomaly"
] | Time series 2 with cutoff anomaly | multiple_choice | 74 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Linear Trend",
"Speed Up/Down Anomaly",
"Cutoff Anomaly",
"Flip Anomaly"
] | You should first identify the time series with the anomaly. Remember, both time series share similar pattern. Then, you should check the type of anomaly based on the given definitions. | Anolmaly Detection | General Anomaly Detection | 230 | [
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] |
What type of noise is present in the given time series? | [
"Gaussian White Noise",
"Red Noise",
"No significant noise"
] | No significant noise | multiple_choice | 62 | medium | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Gaussian White Noise",
"Red Noise"
] | Observe the pattern of fluctuations in the time series. | Noise Understanding | Signal to Noise Ratio Understanding | 231 | [
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] | null |
The given time series has sine wave pattern. How does its amplitude change from the beginning to the end? | [
"Decrease",
"Increase",
"Remain the same"
] | Remain the same | multiple-choice | 17 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Amplitude"
] | Base on the definition of amplitude, check if the distance between the peak and the baseline changes. | Pattern Recognition | Cycle Recognition | 232 | [
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] | null |
Seasonal stationarity refers to a time series where statistical properties remain constant within seasons but may vary between seasons. Does the time series exhibit seasonal stationarity? | [
"No",
"Yes"
] | No | binary | 37 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Stationarity",
"Sine Wave",
"Linear Trend",
"Gaussian White Noise"
] | Determine if the statistical properties of the series are constant within seasons across years. | Pattern Recognition | Stationarity Detection | 233 | [
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] | null |
You are seeing two time series that are random walk. Are they likely to have the same variance? | [
"No, time series 2 has higher variance",
"No, time series 1 has higher variance",
"Yes, they have the same variance"
] | Yes, they have the same variance | multiple_choice | 95 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Red Noise",
"Variance"
] | Random walk is a time series model where the next value is a random walk from the previous value. Variance refers to the distance of the values from the previous steps. At a high level, you should check the distance of the values from the previous steps for both time series. | Similarity Analysis | Distributional | 234 | [
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] |
Two time series are given. Both of them have a noise component. Do they have the same type of noise? | [
"Yes, they both have Gaussian white noise",
"No, they have different noise"
] | No, they have different noise | binary | 87 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Gaussian White Noise",
"Red Noise",
"Additive Composition"
] | When a white noise is added to a time series, it is expected the random fluctuations have similar amplitude or distribution. Random walk, on the other hand, can result in very different noise patterns. | Similarity Analysis | Shape | 235 | [
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] |
What is the direction of the linear trend of the given time series, if any? | [
"Upward",
"Downward",
"No Trend"
] | Upward | multiple_choice | 4 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Linear Trend"
] | Check if the time series values increase or decrease over time. | Pattern Recognition | Trend Recognition | 236 | [
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] | null |
What is the most likely autocorrelation at lag 1 for the given time series? | [
"No autocorrelation",
"High positive autocorrelation",
"Negative autocorrelation"
] | Negative autocorrelation | multiple_choice | 46 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Autocorrelation"
] | While it is hard to directly measure the autocorrelation for higher order lags, the autocorrelation at lag 1 can be approximated by observing the time series pattern. You can tell this by checking the sign and magnitude changes at each step compared to the previous step. | Pattern Recognition | AR/MA recognition | 237 | [
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] | null |
Both time series have a cyclic components. Which time series has a higher amplitude of the cyclic component? | [
"Time series 1 has higher amplitude",
"Time series 2 has higher amplitude"
] | Time series 1 has higher amplitude | binary | 84 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Square Wave",
"Amplitude"
] | Amplitude refers to the height of the peak and the depth of the trough in the cyclic component. You should check the height of the peak and the depth of the trough for both time series. | Similarity Analysis | Shape | 238 | [
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] |
You are given two time series with same underlying pattern but different noise level. Which time series has higher magnitude of noise? | [
"Time series 1",
"Time series 2"
] | Time series 2 | multiple_choice | 61 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Exponential Trend",
"Gaussian White Noise",
"Variance"
] | When the noise level is high, it can distort the pattern in the time series. Both time series have the same underlying pattern, but different noise level. To tell which time series has higher noise level, you should check the degree of distortion of the time series pattern. | Noise Understanding | Signal to Noise Ratio Understanding | 239 | [
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] |
The given time series is a sine wave. What is the most likely amplitude of the sine wave? | [
"7.56",
"8.39",
"1.72"
] | 7.56 | multiple-choice | 21 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Amplitude"
] | Check the distance between the peak and the baseline. | Pattern Recognition | Cycle Recognition | 240 | [
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] | null |
The given time series is a random walk process. What is the most likely noise level? | [
"3.71",
"1.04",
"6.44"
] | 1.04 | multiple_choice | 54 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Red Noise"
] | The noise level refers to the standard deviation of the noise. You should check the degree of variation of the time series over time. You can estimate the standard deviation by observing the average distance between the data points and the past value. | Noise Understanding | Red Noise Recognition | 241 | [
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] | null |
You are given two time series following similar pattern. One has an anomaly and the other does not. Which time series has the anomaly, and what is the likely type of anomaly? | [
"Time series 2 with cutoff anomaly",
"Time series 1 with speed up/down anomaly",
"Time series 1 with flip anomaly"
] | Time series 2 with cutoff anomaly | multiple_choice | 73 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Linear Trend",
"Speed Up/Down Anomaly",
"Cutoff Anomaly",
"Flip Anomaly"
] | You should first identify the time series with the anomaly. Remember, both time series share similar pattern. Then, you should check the type of anomaly based on the given definitions. | Anolmaly Detection | General Anomaly Detection | 242 | [
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] |
The following time series has an anomaly. What is the most likely type of anomaly? | [
"Wander: the pattern deviates off for certain point in time",
"Cutoff: the pattern of time series disappeared for certain point in time",
"Scale: the pattern is at obviously different scale at certain point in time"
] | Wander: the pattern deviates off for certain point in time | multiple_choice | 65 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Cutoff Anomaly",
"Scale Anomaly",
"Wander Anomaly"
] | Anomaly is an observation that deviates from the general pattern in the time series. You should check if the time series has any sudden changes or unexpected patterns. If so, check the type of anomaly based on the given definitions. | Anolmaly Detection | General Anomaly Detection | 243 | [
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] | null |
Is time series 2 a lagged version of time series 1? | [
"Yes",
"No, time series 1 is a lagged version of time series 2",
"No, they do not share similar pattern"
] | Yes | multiple_choice | 98 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Lagged Pair"
] | Focus on the time delay between the two time series. If time series 2 is a lagged version, then it should look the same to time series 1 after being shifted by a certain number of steps. Can you check this? | Causality Analysis | Granger Causality | 244 | [
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] |
Is the given time series stationary? | [
"Yes",
"No"
] | Yes | binary | 31 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Stationarity"
] | Try to see if the time series has a constant mean, and degree of variation over time. | Pattern Recognition | Stationarity Detection | 245 | [
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] | null |
The time series shows a structural break. What is the most likely cause of this break? | [
"Abrupt frequency change",
"Change in variance in underlying distribution",
"Sudden shift in trend direction"
] | Sudden shift in trend direction | multiple_choice | 71 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Linear Trend",
"Gaussian White Noise",
"Sine Wave"
] | You know the time series shows a structural break. Can you first identify the place where the break happens? Then, you should check the type of break based on the given options. | Anolmaly Detection | General Anomaly Detection | 246 | [
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] | null |
What is the primary cyclic pattern observed in the time series? | [
"SquareWave",
"No Pattern at all",
"SineWave",
"SawtoothWave"
] | SquareWave | multiple-choice | 15 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Square Wave",
"Sawtooth Wave"
] | Check the overall shape of the time series against the definition of provided concepts | Pattern Recognition | Cycle Recognition | 247 | [
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] | null |
Which of the given time series has the highest variance? | [
"Time Series 2",
"Time Series 1"
] | Time Series 1 | multiple_choice | 45 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Variance"
] | Check the degree of variation of the time series over time. | Pattern Recognition | First Two Moment Recognition | 248 | [
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] |
Does the given time series exhibit regime switching? | [
"Yes",
"No"
] | Yes | binary | 39 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Regime Switching"
] | Identify whether the time series exhibit different patterns over time. | Pattern Recognition | Regime Switching Detection | 249 | [
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] | null |
The following time series has a noise component. Is it a white noise or random walk? | [
"Random Walk",
"White Noise"
] | White Noise | binary | 52 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Red Noise",
"Gaussian White Noise"
] | White noise is a stationary process with a constant mean and variance. You should check if the time series has a constant mean and variance over time. This can help you distinguish between white noise and random walk. | Noise Understanding | White Noise Recognition | 250 | [
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] | null |
What is the direction of the linear trend of the given time series, if any? | [
"Downward",
"No Trend",
"Upward"
] | No Trend | multiple_choice | 4 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Linear Trend"
] | Check if the time series values increase or decrease over time. | Pattern Recognition | Trend Recognition | 251 | [
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9.641762159785564
] | null |
The given time series has an increasing trend, is it a linear trend or log trend? | [
"Linear",
"Log"
] | Log | multiple_choice | 7 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Linear Trend",
"Log Trend"
] | Check if the slope of the time series is constant or changes over time. | Pattern Recognition | Trend Recognition | 252 | [
-0.02760110617164809,
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1.7514299500013195,
1.856873604855954,
1.7962181358994114,
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] | null |
The given time series has sine wave pattern. How does its amplitude change from the beginning to the end? | [
"Decrease",
"Increase",
"Remain the same"
] | Remain the same | multiple-choice | 18 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Amplitude"
] | Base on the definition of amplitude, check if the distance between the peak and the baseline changes. | Pattern Recognition | Cycle Recognition | 253 | [
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] | null |
Does time series 1 granger cause time series 2? | [
"Yes, time series 1 granger causes time series 2",
"No, time series 2 granger causes time series 1",
"No, they are not granger causality"
] | No, they are not granger causality | binary | 101 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Granger Causality"
] | Granger causality is a statistical concept that determines whether one time series can predict another. While you cannot perform the statistical test, you can check if one time series can predict the other by shifting the time series by a certain number of steps. Do they look simiar after the shift? | Causality Analysis | Granger Causality | 254 | [
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] |
Two time series are given. Both of them have a noise component. Do they have the same level of noise? | [
"Yes, they both have the same level of noise",
"No, they have different level of noise"
] | No, they have different level of noise | binary | 89 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Gaussian White Noise",
"Variance"
] | Noise level refers to the amplitude of the random fluctuations in the time series. Both time series have a white noise component added to it. You should check the amplitude of the noise for both time series. | Similarity Analysis | Shape | 255 | [
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] |
In which part of the time series does the anomaly occur? | [
"Middle",
"End",
"Beginning"
] | Middle | multiple_choice | 78 | medium | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Linear Trend",
"Spike Anomaly",
"Cutoff Anomaly",
"Wander Anomaly"
] | Identify where in the time series sequence the unusual pattern or disruption occurs. | Anolmaly Detection | General Anomaly Detection | 256 | [
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] | null |
What type of trend does the time series exhibit in the latter half? | [
"No trend",
"Exponential",
"Linear"
] | No trend | multiple_choice | 14 | medium | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Linear Trend",
"Exponential Trend"
] | Focus on the pattern of growth or decline in the second half of the time series. | Pattern Recognition | Trend Recognition | 257 | [
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] | null |
Is the given time series likely to be stationary after removing the trend? | [
"Yes",
"No"
] | No | binary | 35 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Stationarity",
"Linear Trend",
"Exponential Trend"
] | Trend brings the overall shape of the time series up or down. Assume this effect is removed, does the time series satisfy the stationarity condition? | Pattern Recognition | Stationarity Detection | 258 | [
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] | null |
The given time series has square wave pattern. How does its period change from the beginning to the end? | [
"Remain the same",
"Decrease",
"Increase"
] | Increase | multiple-choice | 19 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Square Wave",
"Period"
] | Base on the definition of period, check if the time interval between two peaks remains the same. | Pattern Recognition | Cycle Recognition | 259 | [
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] | null |
Is the given time series likely to have a non-stationary anomaly? | [
"No, the anomaly is stationary",
"Yes, due to cutoff",
"Yes, due to trend reversal"
] | Yes, due to cutoff | binary | 70 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Stationarity",
"Linear Trend",
"Sine Wave",
"Cutoff Anomaly",
"Spike Anomaly"
] | Non-stationary anomaly refers to the anomaly that changes over time. You should check if the time series has a constant mean and variance over time. If not, you should check the type of anomaly based on the given definitions. For example, spikes anomaly are stationary. | Anolmaly Detection | General Anomaly Detection | 260 | [
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] | null |
The given time series has multiple trends followed by each other, what is the correct ordering of the trend components? | [
"Linear -> Exponential",
"Linear -> Exponential -> Log",
"Exponential -> Linear -> Log",
"Log"
] | Linear -> Exponential -> Log | multiple_choice | 9 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Linear Trend",
"Exponential Trend",
"Log Trend"
] | Identify the different components first, and then check the assignment of each component. | Pattern Recognition | Trend Recognition | 261 | [
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Is time series 2 a lagged version of time series 1? | [
"No, time series 1 is a lagged version of time series 2",
"No, they do not share similar pattern",
"Yes"
] | Yes | multiple_choice | 96 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Lagged Pair"
] | Focus on the time delay between the two time series. If time series 2 is a lagged version, then it should look the same to time series 1 after being shifted by a certain number of steps. Can you check this? | Causality Analysis | Granger Causality | 262 | [
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One type of noise in time series is random walk. Is the given time series noisy based on your understanding of random walk | [
"No",
"Yes"
] | No | binary | 56 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Red Noise"
] | When we say a time series is noisy, it typically refers to there are random fluctuations that disrupt the overal pattern of the time series. When the time series has a random walk noise applied to it, it seems like the pattern are even more disrupted. Can you check if it is the case for the given time series? | Noise Understanding | Signal to Noise Ratio Understanding | 263 | [
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] | null |
What is the most likely mean of the given time series? | [
"-19.1",
"24.26",
"6.31"
] | 24.26 | multiple_choice | 41 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Mean"
] | The given time series is stationary. Check the average value of the time series over time. | Pattern Recognition | First Two Moment Recognition | 264 | [
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] | null |
You are given two time series following similar pattern. Both of them have an anomaly. What is the likely type of anomaly in each time series? | [
"Time series 1 with cutoff anomaly and time series 2 with speed up/down anomaly",
"Time series 1 with flip anomaly and time series 2 with speed up/down anomaly",
"Time series 1 with cutoff anomaly and time series 2 with flip anomaly"
] | Time series 1 with cutoff anomaly and time series 2 with speed up/down anomaly | multiple_choice | 75 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Sawtooth Wave",
"Linear Trend",
"Log Trend",
"Cutoff Anomaly",
"Flip Anomaly",
"Speed Up/Down Anomaly"
] | You already know both time series have an anomaly. You should treat them separately and check the type of anomaly based on the given definitions. | Anolmaly Detection | General Anomaly Detection | 265 | [
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] |
Both time series have a cyclic components. Which time series has a higher amplitude of the cyclic component? | [
"Time series 2 has higher amplitude",
"Time series 1 has higher amplitude"
] | Time series 2 has higher amplitude | binary | 84 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Square Wave",
"Amplitude"
] | Amplitude refers to the height of the peak and the depth of the trough in the cyclic component. You should check the height of the peak and the depth of the trough for both time series. | Similarity Analysis | Shape | 266 | [
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] |
Which of the following best describe the cycle pattern in the given time series? | [
"Amplitude decrease over time",
"Amplitude remain the same over time",
"Amplitude increase over time"
] | Amplitude increase over time | multiple-choice | 29 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Amplitude"
] | Check the distance between the peak and the baseline, and see how it changes over time. | Pattern Recognition | Cycle Recognition | 267 | [
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] | null |
Which additive combination of patterns best describes the time series? | [
"SineWave + SawtoothWave",
"SawtoothWave + SquareWave",
"SineWave + SquareWave"
] | SineWave + SawtoothWave | multiple-choice | 17 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Square Wave",
"Sawtooth Wave",
"Additive Composition"
] | Imagine the shape of the time series as addition of two different patterns. | Pattern Recognition | Cycle Recognition | 268 | [
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] | null |
The time series has three cyclic pattern composed additively. Which cycle pattern is most dominant in the given time series? | [
"SquareWave",
"SineWave",
"SawtoothWave"
] | SineWave | multiple-choice | 20 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Square Wave",
"Sawtooth Wave",
"Additive Composition",
"Amplitude"
] | The cyclic patterns have different period and amplitude. The dominant pattern is the one that has the highest amplitude. Identify the pattern with the highest peak. | Pattern Recognition | Cycle Recognition | 269 | [
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] | null |
Which of the given time series has the highest variance? | [
"Time Series 2",
"Time Series 1"
] | Time Series 1 | multiple_choice | 44 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Variance"
] | Check the degree of variation of the time series over time. | Pattern Recognition | First Two Moment Recognition | 270 | [
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You are given two time series which both have a trend component. Do they share the same direction of trend? | [
"Yes, they have the same direction of trend",
"No, they have different direction of trend"
] | Yes, they have the same direction of trend | binary | 82 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Linear Trend",
"Sine Wave"
] | Trend refers to the general direction of the time series. Are the values going up or down? Check this for both time series to see if they have the same direction of trend. | Similarity Analysis | Shape | 271 | [
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] |
One type of noise in time series is random walk. Is the given time series noisy based on your understanding of random walk | [
"No",
"Yes"
] | No | binary | 57 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Red Noise"
] | When we say a time series is noisy, it typically refers to there are random fluctuations that disrupt the overal pattern of the time series. When the time series has a random walk noise applied to it, it seems like the pattern are even more disrupted. Can you check if it is the case for the given time series? | Noise Understanding | Signal to Noise Ratio Understanding | 272 | [
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] | null |
Given that following time series exhibit piecewise linear trend, how many pieces are there? | [
"2",
"1",
"4"
] | 4 | multiple_choice | 5 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Piecewise Linear Trend"
] | Check if the time series values increase or decrease linearly over time with different slopes. The slope change could be both positive and negative. | Pattern Recognition | Trend Recognition | 273 | [
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] | null |
Piece-wise stationarity means a time series is stationary in distinct segments, with abrupt changes between segments. Each segment has its own constant statistical properties. Does the time series exhibit piecewise stationarity? | [
"No",
"Yes"
] | No | binary | 38 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Stationarity",
"Linear Trend",
"Gaussian White Noise"
] | Look for segments of the time series that are individually stationary, even if the whole series is not. | Pattern Recognition | Stationarity Detection | 274 | [
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] | null |
The following time series has two types of anomalies appearing at different time points. What are the likely types of anomalies? | [
"speedup and cutoff",
"cutoff and flip",
"speedup and flip"
] | speedup and flip | multiple_choice | 69 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Cutoff Anomaly",
"Flip Anomaly",
"Speed Up/Down Anomaly"
] | You should first identify the two places where the anomalies appear. Then, you should check the type of anomaly based on the given definitions. | Anolmaly Detection | General Anomaly Detection | 275 | [
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] | null |
The given time series is a sawtooth wave. What is the most likely amplitude of the sawtooth wave? | [
"1.5",
"17.47",
"4.09"
] | 4.09 | multiple-choice | 23 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sawtooth Wave",
"Amplitude"
] | Check the distance between the peak and the baseline. | Pattern Recognition | Cycle Recognition | 276 | [
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] | null |
Both time series have a cyclic components. Which time series has a higher amplitude of the cyclic component? | [
"Time series 1 has higher amplitude",
"Time series 2 has higher amplitude"
] | Time series 1 has higher amplitude | binary | 84 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Square Wave",
"Amplitude"
] | Amplitude refers to the height of the peak and the depth of the trough in the cyclic component. You should check the height of the peak and the depth of the trough for both time series. | Similarity Analysis | Shape | 277 | [
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] |
Both time series have a cyclic components. Which time series has a higher amplitude of the cyclic component? | [
"Time series 1 has higher amplitude",
"Time series 2 has higher amplitude"
] | Time series 2 has higher amplitude | binary | 83 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Square Wave",
"Amplitude"
] | Amplitude refers to the height of the peak and the depth of the trough in the cyclic component. You should check the height of the peak and the depth of the trough for both time series. | Similarity Analysis | Shape | 278 | [
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] |
What is the most likely linear trend coefficient of the given time series? | [
"1.38",
"9.29",
"0"
] | 9.29 | multiple_choice | 2 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Linear Trend"
] | The bigger the slope of the line, the higher the trend coefficient. | Pattern Recognition | Trend Recognition | 279 | [
-0.3684483557598476,
0.5213425804322104,
-0.19795209558101412,
2.1735850491427064,
0.038957697152314286,
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] | null |
You are given two time series following similar pattern. One has an anomaly and the other does not. Which time series has the anomaly, and what is the likely type of anomaly? | [
"Time series 2 with cutoff anomaly",
"Time series 1 with flip anomaly",
"Time series 1 with speed up/down anomaly"
] | Time series 1 with speed up/down anomaly | multiple_choice | 73 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Linear Trend",
"Speed Up/Down Anomaly",
"Cutoff Anomaly",
"Flip Anomaly"
] | You should first identify the time series with the anomaly. Remember, both time series share similar pattern. Then, you should check the type of anomaly based on the given definitions. | Anolmaly Detection | General Anomaly Detection | 280 | [
0,
0.2608388997549282,
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0.6986838817581067,
0.8384912828668734,
0.9062993580047232,
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Two time series are given. Both of them have a noise component. Do they have the same level of noise? | [
"No, they have different level of noise",
"Yes, they both have the same level of noise"
] | Yes, they both have the same level of noise | binary | 89 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Gaussian White Noise",
"Variance"
] | Noise level refers to the amplitude of the random fluctuations in the time series. Both time series have a white noise component added to it. You should check the amplitude of the noise for both time series. | Similarity Analysis | Shape | 281 | [
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] |
The given time series has sine wave pattern. How does its amplitude change from the beginning to the end? | [
"Increase",
"Decrease",
"Remain the same"
] | Decrease | multiple-choice | 18 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Amplitude"
] | Base on the definition of amplitude, check if the distance between the peak and the baseline changes. | Pattern Recognition | Cycle Recognition | 282 | [
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] | null |
Does time series 1 granger cause time series 2? | [
"Yes, time series 1 granger causes time series 2",
"No, they are not granger causality",
"No, time series 2 granger causes time series 1"
] | No, time series 2 granger causes time series 1 | binary | 103 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Granger Causality"
] | Granger causality is a statistical concept that determines whether one time series can predict another. While you cannot perform the statistical test, you can check if one time series can predict the other by shifting the time series by a certain number of steps. Do they look simiar after the shift? | Causality Analysis | Granger Causality | 283 | [
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You are given two time series following similar pattern. Both of them have an anomaly. What is the likely type of anomaly in each time series? | [
"Time series 1 with cutoff anomaly and time series 2 with flip anomaly",
"Time series 1 with cutoff anomaly and time series 2 with speed up/down anomaly",
"Time series 1 with flip anomaly and time series 2 with speed up/down anomaly"
] | Time series 1 with cutoff anomaly and time series 2 with flip anomaly | multiple_choice | 74 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Sawtooth Wave",
"Linear Trend",
"Log Trend",
"Cutoff Anomaly",
"Flip Anomaly",
"Speed Up/Down Anomaly"
] | You already know both time series have an anomaly. You should treat them separately and check the type of anomaly based on the given definitions. | Anolmaly Detection | General Anomaly Detection | 284 | [
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] |
What is the most dominant pattern in this complex time series? | [
"Trend",
"Seasonality",
"Noise"
] | Seasonality | multiple_choice | 14 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Linear Trend",
"Sine Wave",
"Gaussian White Noise"
] | Identify which component (trend, seasonality, or noise) has the largest impact on the overall pattern. | Pattern Recognition | Trend Recognition | 285 | [
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] | null |
What is the most likely linear trend coefficient of the given time series? | [
"6.12",
"0",
"1.53"
] | 6.12 | multiple_choice | 2 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Linear Trend"
] | The bigger the slope of the line, the higher the trend coefficient. | Pattern Recognition | Trend Recognition | 286 | [
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] | null |
The given time series has multiple cycle patterns with same amplitude and period. How are they combined together? | [
"Additive",
"Multiplicative"
] | Additive | multiple-choice | 27 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Square Wave",
"Sawtooth Wave",
"Additive Composition",
"Multiplicative Composition"
] | For additive composition, the patterns are added together. This changes amplitude. For multiplicative composition, the overall shape of the time series might be distorted with cyclic patterns unobservable. | Pattern Recognition | Cycle Recognition | 287 | [
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] | null |
One type of noise in time series is white noise. Is the given time series noisy based on your understanding of white noise? | [
"Yes",
"No"
] | Yes | binary | 55 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Gaussian White Noise"
] | When we say a time series is noisy, it typically refers to there are random fluctuations that disrupt the overal pattern of the time series. Can you check if it is the case for the given time series? | Noise Understanding | Signal to Noise Ratio Understanding | 288 | [
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] | null |
What is the most likely linear trend coefficient of the given time series? | [
"0",
"9.46",
"4.07"
] | 9.46 | multiple_choice | 2 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Linear Trend"
] | The bigger the slope of the line, the higher the trend coefficient. | Pattern Recognition | Trend Recognition | 289 | [
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] | null |
What type of trend does the time series exhibit in the latter half? | [
"No trend",
"Linear",
"Exponential"
] | Linear | multiple_choice | 15 | medium | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Linear Trend",
"Exponential Trend"
] | Focus on the pattern of growth or decline in the second half of the time series. | Pattern Recognition | Trend Recognition | 290 | [
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1.677412814623971,
1.5836209162907748,
1.641439656847144,
1.5971060732845463
] | null |
Is the given time series likely to have an anomaly? | [
"Yes, it's pattern is flipped at certain point in time",
"No",
"Yes, it's pattern is distorted by random spikes"
] | Yes, it's pattern is flipped at certain point in time | binary | 64 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Flip Anomaly",
"Spike Anomaly"
] | Anomaly is an observation that deviates from the general pattern in the time series. You should check if the time series has any sudden changes or unexpected patterns. If so, check the type of anomaly based on the given definitions. | Anolmaly Detection | General Anomaly Detection | 291 | [
0,
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7.037951905013582,
6.789222533946849
] | null |
The given time series has multiple cycle patterns with same amplitude and period. How are they combined together? | [
"Multiplicative",
"Additive"
] | Additive | multiple-choice | 28 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Square Wave",
"Sawtooth Wave",
"Additive Composition",
"Multiplicative Composition"
] | For additive composition, the patterns are added together. This changes amplitude. For multiplicative composition, the overall shape of the time series might be distorted with cyclic patterns unobservable. | Pattern Recognition | Cycle Recognition | 292 | [
-2.601807999288534,
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] | null |
The given time series is a swatooth wave followed by a square wave. What is the most likely period of the swatooth wave? | [
"17.89",
"34.11",
"51.17"
] | 34.11 | multiple-choice | 25 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sawtooth Wave",
"Square Wave",
"Period"
] | The sawtooth wave comes before the square wave. Begin by identifying where the sawtooth wave starts. Next, measure the time interval between two peaks. | Pattern Recognition | Cycle Recognition | 293 | [
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] | null |
The following time series has an anomaly. What is the most likely type of anomaly? | [
"Wander: the pattern deviates off for certain point in time",
"Scale: the pattern is at obviously different scale at certain point in time",
"Cutoff: the pattern of time series disappeared for certain point in time"
] | Scale: the pattern is at obviously different scale at certain point in time | multiple_choice | 66 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Cutoff Anomaly",
"Scale Anomaly",
"Wander Anomaly"
] | Anomaly is an observation that deviates from the general pattern in the time series. You should check if the time series has any sudden changes or unexpected patterns. If so, check the type of anomaly based on the given definitions. | Anolmaly Detection | General Anomaly Detection | 294 | [
-1.5426980635477918,
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] | null |
What is the direction of the linear trend of the given time series, if any? | [
"Downward",
"No Trend",
"Upward"
] | Downward | multiple_choice | 4 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Linear Trend"
] | Check if the time series values increase or decrease over time. | Pattern Recognition | Trend Recognition | 295 | [
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] | null |
You are given two time series following similar pattern. One has an anomaly and the other does not. Which time series has the anomaly, and what is the likely type of anomaly? | [
"Time series 1 with speed up/down anomaly",
"Time series 1 with flip anomaly",
"Time series 2 with cutoff anomaly"
] | Time series 1 with speed up/down anomaly | multiple_choice | 73 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Linear Trend",
"Speed Up/Down Anomaly",
"Cutoff Anomaly",
"Flip Anomaly"
] | You should first identify the time series with the anomaly. Remember, both time series share similar pattern. Then, you should check the type of anomaly based on the given definitions. | Anolmaly Detection | General Anomaly Detection | 296 | [
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] |
The given time series is a sine wave. What is the most likely amplitude of the sine wave? | [
"7.24",
"6.04",
"1.72"
] | 7.24 | multiple-choice | 22 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Amplitude"
] | Check the distance between the peak and the baseline. | Pattern Recognition | Cycle Recognition | 297 | [
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1.5527672726927617,
2.873285918319409,
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] | null |
Does the following time series exhibit a mean reversion property? | [
"Yes",
"No"
] | Yes | binary | 47 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Mean Reversion"
] | Mean reversion first requires the time series have constant mean. You should check this first. Then, see if the time series tends to revert back to the mean after a shock. | Pattern Recognition | AR/MA recognition | 298 | [
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] | null |
Are the given two time series likely to have the same underlying distribution? | [
"Yes, they have the same underlying distribution",
"No, they have different underlying distribution"
] | Yes, they have the same underlying distribution | binary | 97 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Red Noise",
"AutoRegressive Process",
"Linear Trend"
] | When we say two time series have the same underlying distribution, you should check if they have the same mean and variance. They should also share similar behaviors over time. | Similarity Analysis | Distributional | 299 | [
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] |
Does the given time series exhibit regime switching? | [
"Yes",
"No"
] | Yes | binary | 39 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Regime Switching"
] | Identify whether the time series exhibit different patterns over time. | Pattern Recognition | Regime Switching Detection | 300 | [
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] | null |