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In which part of the time series does the anomaly occur? | [
"Beginning",
"Middle",
"End"
] | Beginning | 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 | 1 | [
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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? | [
"cutoff and flip",
"speedup and flip",
"speedup and cutoff"
] | cutoff 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 | 2 | [
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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 | 3 | [
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You are given two Autoregressive processes AR(1). Which of the following time series has higher standard deviation for their random component? | [
"Time series 1",
"Time series 2"
] | Time series 1 | 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. | [
"AutoRegressive Process",
"Variance"
] | The standard deviation of the noise component is related to the average distance between the data points and their past values. You should check the degree of variation of the time series over time. Which time series has a higher change in average? | Noise Understanding | Signal to Noise Ratio Understanding | 4 | [
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] |
What is the most dominant pattern in this complex time series? | [
"Noise",
"Trend",
"Seasonality"
] | Seasonality | 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 | 5 | [
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] | null |
Is the given time series likely to be stationary after removing the cycle component? | [
"Yes",
"No"
] | Yes | binary | 36 | 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",
"Sine Wave",
"Square Wave"
] | Cycle component brings the cyclic pattern to the time series. Assume this effect is removed, does the time series satisfy the stationarity condition? | Pattern Recognition | Stationarity Detection | 6 | [
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] | null |
The given time series is a sine wave followed by a square wave. What is the most likely amplitude of the square wave? | [
"17.92",
"5.37",
"1.63"
] | 5.37 | multiple-choice | 24 | 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",
"Amplitude"
] | After the sine wave, the square wave follows. Begin by identifying where the square wave starts. Next, measure the distance between its peak and baseline. | Pattern Recognition | Cycle Recognition | 7 | [
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] | null |
Which of the following time series is more likely to be an MA(1) process? | [
"Time Series 2",
"Time Series 1"
] | Time Series 2 | multiple_choice | 50 | 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. | [
"Moving Average Process",
"Stationarity"
] | MA(1) process 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. The other option is likely not stationary. | Pattern Recognition | AR/MA recognition | 8 | [
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Based on the given time series, how many different regimes are there? | [
"3",
"4",
"1"
] | 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 | 9 | [
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] | null |
Are there any granger causality between the two time series? | [
"Yes, time series 1 granger causes time series 2",
"No, they are not granger causality",
"Yes, time series 2 granger causes time series 1"
] | Yes, time series 1 granger causes time series 2 | binary | 105 | 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 | 10 | [
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] |
What is the direction of the linear trend of the given time series, if any? | [
"No Trend",
"Upward",
"Downward"
] | 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 | 11 | [
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] | null |
What type of trend does the time series exhibit in the latter half? | [
"Linear",
"Exponential",
"No trend"
] | 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 | 12 | [
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] | null |
How does the noise in the given time series influence the detection of periodic pattern in the time series? | [
"Distort the pattern",
"No influence"
] | Distort the pattern | binary | 58 | 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",
"Sine Wave",
"Additive Composition"
] | When the noise level is high, it can distort the pattern in the time series. Can you check if you can still detect the cyclic pattern in the time series? | Noise Understanding | Signal to Noise Ratio Understanding | 13 | [
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] | null |
Two time series are given. One has noise and the other does not. Do they have similar pattern? | [
"Yes, they have similar pattern",
"No, they have different pattern"
] | Yes, they have similar pattern | binary | 82 | 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"
] | 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 | 14 | [
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The given time series has a decreasing trend, is it a linear trend or log trend? | [
"Log",
"Linear"
] | Linear | multiple_choice | 8 | 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 | 15 | [
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] | null |
Is the two time series lagged version of each other despite amplitude difference? | [
"No, they are not lagged versions",
"Yes, they are lagged versions"
] | No, they are not lagged versions | 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. | [
"Lagged Pair"
] | Try to shift one time series by a certain number of steps and check if it looks the same as the other time series despite the scale difference. If they are lagged versions, they should look very similar in general after the shift. | Causality Analysis | Granger Causality | 16 | [
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] |
What is the type of the trend of the given time series? | [
"No Trend",
"Exponential",
"Linear"
] | Linear | 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 | 17 | [
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] | null |
The following time series has an anomaly where the pattern is cutoff at certain point in time. What is the likely pattern of the time series without the anomaly? | [
"Square wave with log trend",
"Sawtooth wave with exponential trend",
"Sine wave with linear trend"
] | Sawtooth wave with exponential trend | multiple_choice | 67 | 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",
"Cutoff Anomaly"
] | Cutoff anomaly brings sudden disappearance of the pattern. However, this only influences a small part of the time series. Can you check the place where the pattern disappears and try to recover the original pattern? | Anolmaly Detection | General Anomaly Detection | 18 | [
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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 | 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. | [
"Additive Composition",
"Multiplicative Composition",
"Gaussian White Noise"
] | Additive noise is added to the signal, while multiplicative noise is multiplied to the signal. When a cyclic component is added with a white noise, the cyclic pattern still remains. When a cyclic 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 | 19 | [
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] | null |
Is the given time series likely to be a random walk process? | [
"Yes",
"No"
] | Yes | binary | 53 | 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"
] | Random walk is a non-stationary process with a constant mean and variance. You should check if the time series has a constant mean and variance over time. Another important property is that the noise is correlated over time. Does the time series seem to have these properties? | Noise Understanding | Red Noise Recognition | 20 | [
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] | null |
What type of noise is present in the given time series? | [
"No significant noise",
"Gaussian White Noise",
"Red 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 | 21 | [
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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? | [
"No, they have different noise",
"Yes, they both have Gaussian white 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 | 22 | [
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1.0884518848875122
] |
In which part of the time series does the anomaly occur? | [
"End",
"Beginning",
"Middle"
] | 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 | 23 | [
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] | null |
Based on the given time series, how many different regimes are there? | [
"3",
"4",
"1"
] | 1 | multiple_choice | 41 | 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 | 24 | [
-0.16436062447256977,
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1.8544068346251905,
1.9573028942908768,
1.8990668319184862,
1.9191442620528891
] | null |
The given time series has multiple trends followed by each other, what is the correct ordering of the trend components? | [
"Exponential -> Linear -> Log",
"Linear -> Exponential -> Log",
"Linear -> Exponential",
"Log"
] | Linear -> Exponential | 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 | 25 | [
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18.766112418248753,
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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"
] | Decrease | 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 | 26 | [
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] | null |
Is the given time series likely to be stationary after differencing? | [
"Yes",
"No"
] | No | 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"
] | Differencing is a common technique to make a time series stationary. Focus on checking if the trend is removed after differencing. | Pattern Recognition | Stationarity Detection | 27 | [
1.5117131833080868,
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] | null |
The given time series has a cycle component and a trend component. Is it an additive or multiplicative model? | [
"Multiplicative",
"Additive"
] | Multiplicative | multiple_choice | 11 | 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",
"Additive Composition",
"Multiplicative Composition"
] | For a multiplicative composition, the amplitude of the cyclic component will increase or decrease depending on the trend component. | Pattern Recognition | Trend Recognition | 28 | [
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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 flip",
"speedup and cutoff",
"cutoff and flip"
] | speedup and cutoff | 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 | 29 | [
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] | null |
The given time series is a sine wave followed by a square wave patterns with different amplitude. How does the amplitude vary over time? | [
"Remain the same",
"Decrease",
"Increase"
] | Decrease | 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. | [
"Sine Wave",
"Square Wave",
"Amplitude"
] | Focus on the amplitude instead of cyclic pattern change, check if the distance between the peak and the baseline changes. | Pattern Recognition | Cycle Recognition | 30 | [
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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 2 has higher amplitude",
"Time series 1 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 | 31 | [
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] |
Is the given time series likely to be stationary after differencing? | [
"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"
] | Differencing is a common technique to make a time series stationary. Focus on checking if the trend is removed after differencing. | Pattern Recognition | Stationarity Detection | 32 | [
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] | null |
Covariance stationarity in a time series means constant mean, constant variance and that autocovariance depends only on time lag, not absolute time. Is the given time series covariance-stationary? | [
"Yes",
"No"
] | Yes | binary | 36 | 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",
"AutoRegressive Process",
"Linear Trend"
] | Check if the covariance between any two points depends only on the time distance between them. | Pattern Recognition | Stationarity Detection | 33 | [
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] | null |
What is the direction of the linear trend of the given time series, if any? | [
"Downward",
"Upward",
"No Trend"
] | 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 | 34 | [
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] | null |
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 | 35 | [
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] |
The given time series has a trend and a cyclic component. It also has an anomaly. What is the most likely combination of components without the anomaly? | [
"Log trend and sawtooth wave",
"Exponential trend and square wave",
"Linear trend and sine wave"
] | Log trend and sawtooth wave | multiple_choice | 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. | [
"Linear Trend",
"Sine Wave",
"Exponential Trend",
"Square Wave",
"Log Trend",
"Sawtooth Wave",
"Cutoff Anomaly",
"Flip Anomaly"
] | The anomaly only influences a small part of the time series. You should focus on the overall pattern of the time series without the anomaly. Can you recover the original pattern? | Anolmaly Detection | General Anomaly Detection | 36 | [
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] | null |
What is the type of the trend of the given time series? | [
"Exponential",
"Linear",
"No Trend"
] | Exponential | 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 | 37 | [
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] | null |
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 cutoff | binary | 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. | [
"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 | 38 | [
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] | null |
Is time series 1 a lagged version of time series 2? | [
"Yes",
"No, they do not share similar pattern",
"No, time series 2 is a lagged version of time series 1"
] | Yes | multiple_choice | 99 | 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 1 is a lagged version, then it should look the same to time series 2 after being shifted by a certain number of steps. Can you check this? | Causality Analysis | Granger Causality | 39 | [
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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"
] | No, they have different underlying distribution | binary | 94 | 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. | [
"AutoRegressive Process",
"Moving Average Process"
] | The difference between AR(1) and MA(1) is that AR(1) is a linear combination of past values and white noise, while MA(1) is a linear combination of past white noise values. You should check if the time series exhibit any dependency on the previous values. This could give you a clue about whether the time series is AR(1) or not. Check this for both time series. | Similarity Analysis | Distributional | 40 | [
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] |
What is the primary cyclic pattern observed in the time series? | [
"SawtoothWave",
"SquareWave",
"No Pattern at all",
"SineWave"
] | SineWave | 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 | 41 | [
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] | null |
The given time series is a random walk process. What is the most likely noise level? | [
"3.8",
"6.88",
"1.91"
] | 3.8 | multiple_choice | 55 | 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 | 42 | [
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] | null |
Covariance stationarity in a time series means constant mean, constant variance and that autocovariance depends only on time lag, not absolute time. Is the given time series covariance-stationary? | [
"Yes",
"No"
] | 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",
"AutoRegressive Process",
"Linear Trend"
] | Check if the covariance between any two points depends only on the time distance between them. | Pattern Recognition | Stationarity Detection | 43 | [
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] | null |
What is the type of the trend of the given time series? | [
"Exponential",
"No Trend",
"Linear"
] | Exponential | 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 | 44 | [
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] | null |
You are given two time series which both have a trend component. Do they share the same direction of trend? | [
"No, they have different direction of trend",
"Yes, they have the same direction of trend"
] | No, they have different direction of trend | binary | 81 | 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 | 45 | [
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] |
The given time series is a sawtooth wave. What is the most likely amplitude of the sawtooth wave? | [
"6.12",
"7.25",
"2.62"
] | 7.25 | 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 | 46 | [
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] | null |
The given time series has an increasing trend, is it a linear trend or log trend? | [
"Log",
"Linear"
] | Linear | 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 | 47 | [
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] | null |
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 | 48 | [
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] | null |
Based on the given time series, how many different regimes are there? | [
"1",
"4",
"3"
] | 3 | multiple_choice | 41 | 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 | 49 | [
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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",
"Yes, they have the same variance",
"No, time series 1 has higher 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 | 50 | [
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What type of trend does the time series exhibit in the latter half? | [
"Exponential",
"No trend",
"Linear"
] | Exponential | 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 | 51 | [
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] | null |
The given time series is a white noise process. What is the most likely noise level? | [
"0.99",
"9.73",
"4.19"
] | 0.99 | 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 | 52 | [
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] | null |
Which of the following time series is more likely to be an MA(1) process? | [
"Time Series 2",
"Time Series 1"
] | Time Series 1 | multiple_choice | 49 | 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. | [
"Moving Average Process",
"Stationarity"
] | MA(1) process 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. The other option is likely not stationary. | Pattern Recognition | AR/MA recognition | 53 | [
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You are given two time series where one is the lagged version of the other. What is the most likely lagging step? | [
"Lagging step is between 5 to 20",
"Lagging step is between 30 to 45",
"Lagging step is between 60 to 75"
] | Lagging step is between 60 to 75 | 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"
] | You already know that one time series is the lagged version of the other. Shift the time series by lags proposed in the options and check which one looks the same as the other time series. | Causality Analysis | Granger Causality | 54 | [
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] |
Does time series 1 granger cause time series 2? | [
"No, time series 2 granger causes time series 1",
"No, they are not granger causality",
"Yes, time series 1 granger causes time series 2"
] | Yes, time series 1 granger causes time series 2 | 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 | 55 | [
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] |
The given time series is a sawtooth wave. What is the most likely amplitude of the sawtooth wave? | [
"1.46",
"7.01",
"8.59"
] | 7.01 | multiple-choice | 24 | 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 | 56 | [
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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? | [
"54.39",
"18.14",
"31.26"
] | 18.14 | 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 | 57 | [
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] | null |
Does the following time series exhibit a mean reversion property? | [
"Yes",
"No"
] | Yes | binary | 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. | [
"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 | 58 | [
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] | null |
What is the primary cyclic pattern observed in the time series? | [
"No Pattern at all",
"SineWave",
"SquareWave",
"SawtoothWave"
] | 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 | 59 | [
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] | null |
Is time series 1 a lagged version of time series 2? | [
"Yes",
"No, they do not share similar pattern",
"No, time series 2 is a lagged version of time series 1"
] | No, time series 2 is a lagged version of time series 1 | multiple_choice | 97 | 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 1 is a lagged version, then it should look the same to time series 2 after being shifted by a certain number of steps. Can you check this? | Causality Analysis | Granger Causality | 60 | [
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] |
The given time series is a swatooth wave followed by a square wave. What is the most likely period of the swatooth wave? | [
"51.95",
"12.52",
"34.34"
] | 51.95 | 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 | 61 | [
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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? | [
"No, they have different noise",
"Yes, they both have Gaussian white noise"
] | Yes, they both have Gaussian white noise | binary | 88 | 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 | 62 | [
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] |
Does time series 1 granger cause time series 2? | [
"No, time series 2 granger causes time series 1",
"Yes, time series 1 granger causes time series 2",
"No, they are not granger causality"
] | 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 | 63 | [
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] |
Is time series 2 a lagged version of time series 1? | [
"No, they do not share similar pattern",
"Yes",
"No, time series 1 is a lagged version of time series 2"
] | No, time series 1 is a lagged version of time series 2 | 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 | 64 | [
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What is the most likely linear trend coefficient of the given time series? | [
"7.1",
"0",
"2.87"
] | 0 | 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 | 65 | [
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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? | [
"Log",
"Exponential",
"Linear"
] | Log | 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 | 66 | [
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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? | [
"14.88",
"33.14",
"53.75"
] | 33.14 | 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 | 67 | [
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] | null |
How does the linear trend in the first half of the time series compare to the trend in the second half? | [
"Different",
"Same"
] | Different | binary | 6 | 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. | [
"Piecewise Linear Trend"
] | Check if the time series is a piecewise linear trend with different slopes in the first and second half. | Pattern Recognition | Trend Recognition | 68 | [
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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"
] | Change in variance in underlying distribution | multiple_choice | 72 | 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 | 69 | [
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] | null |
What is the most dominant pattern in this complex time series? | [
"Seasonality",
"Trend",
"Noise"
] | Noise | 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 | 70 | [
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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 | 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 | 71 | [
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] |
Is the mean stable over time in the given time series? | [
"Yes",
"No"
] | Yes | binary | 44 | 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"
] | Check if the average value of the time series changes over time. | Pattern Recognition | First Two Moment Recognition | 72 | [
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] | null |
The following time series has an anomaly with random large fluctuations. What is the likely pattern of the time series without the anomaly? | [
"Sawtooth wave with linear trend",
"Sine wave with linear trend",
"Square wave with log trend"
] | Sawtooth wave with linear trend | multiple_choice | 67 | 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",
"Spike Anomaly"
] | Spikes anomaly bring constant large random fluctuations. Can you check the place where the spikes disappear and try to recover the original pattern? | Anolmaly Detection | General Anomaly Detection | 73 | [
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] | null |
Which of the following best describe the cycle pattern in the given time series? | [
"Amplitude decrease over time",
"Amplitude increase over time",
"Amplitude remain the same over time"
] | Amplitude decrease over time | 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",
"Amplitude"
] | Check the distance between the peak and the baseline, and see how it changes over time. | Pattern Recognition | Cycle Recognition | 74 | [
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] | null |
You are given two Autoregressive processes AR(1). Which of the following time series has higher standard deviation for their random component? | [
"Time series 1",
"Time series 2"
] | Time series 1 | 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. | [
"AutoRegressive Process",
"Variance"
] | The standard deviation of the noise component is related to the average distance between the data points and their past values. You should check the degree of variation of the time series over time. Which time series has a higher change in average? | Noise Understanding | Signal to Noise Ratio Understanding | 75 | [
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You are given two time series with different underlying functional form. Are they likely to have the same variance? | [
"No, time series have different variance",
"Yes, they have the same variance"
] | No, time series have different variance | binary | 94 | 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",
"Variance"
] | You should focus on the underlying distribution of the time series. You can start from analyzing whether both time series are stationary. Then, you can check if they have the same mean and degree of variation from mean. | Similarity Analysis | Distributional | 76 | [
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] |
You are given two time series with different underlying functional form. Are they likely to have the same variance? | [
"Yes, they have the same variance",
"No, time series have different variance"
] | No, time series have different variance | binary | 94 | 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",
"Variance"
] | You should focus on the underlying distribution of the time series. You can start from analyzing whether both time series are stationary. Then, you can check if they have the same mean and degree of variation from mean. | Similarity Analysis | Distributional | 77 | [
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] |
Is time series 1 a lagged version of time series 2? | [
"No, they do not share similar pattern",
"No, time series 2 is a lagged version of time series 1",
"Yes"
] | No, they do not share similar pattern | multiple_choice | 99 | 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 1 is a lagged version, then it should look the same to time series 2 after being shifted by a certain number of steps. Can you check this? | Causality Analysis | Granger Causality | 78 | [
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] |
What is the most dominant pattern in this complex time series? | [
"Seasonality",
"Trend",
"Noise"
] | Noise | 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 | 79 | [
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] | null |
The given time series is a white noise process. What is the most likely noise level? | [
"0.24",
"9.4",
"4.52"
] | 4.52 | 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 | 80 | [
3.138458720835493,
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] | null |
What is the most likely mean of the given time series? | [
"0.66",
"27.56",
"-14.48"
] | 27.56 | multiple_choice | 42 | 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 | 81 | [
27.310510405517082,
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27.008122908092595,
27.457274224501777,
27.59057237158603
] | null |
The following time series has an anomaly where the pattern is cutoff at certain point in time. What is the likely pattern of the time series without the anomaly? | [
"Square wave with log trend",
"Sine wave with linear trend",
"Sawtooth wave with exponential trend"
] | Sine wave with linear trend | multiple_choice | 68 | 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",
"Cutoff Anomaly"
] | Cutoff anomaly brings sudden disappearance of the pattern. However, this only influences a small part of the time series. Can you check the place where the pattern disappears and try to recover the original pattern? | Anolmaly Detection | General Anomaly Detection | 82 | [
0,
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6.567834306504433,
7.797541089999897,
8.737100556154564,
9.204364421297285
] | null |
What is the most likely variance of the given time series? | [
"0.17",
"varies across time",
"1"
] | varies across time | multiple_choice | 42 | 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 | 83 | [
1.4400660144422108,
1.3924147694941866,
2.924461028391785,
3.152119678955696,
3.972405521006856,
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] | null |
What is the most likely mean of the given time series? | [
"-19.5",
"0.25",
"23.67"
] | 23.67 | 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 | 84 | [
23.9219398213857,
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23.30774045139507,
23.643778758501018,
23.94614463391103,
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23.6731966446555,
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] | null |
Covariance stationarity in a time series means constant mean, constant variance and that autocovariance depends only on time lag, not absolute time. Is the given time series covariance-stationary? | [
"No",
"Yes"
] | Yes | binary | 36 | 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",
"AutoRegressive Process",
"Linear Trend"
] | Check if the covariance between any two points depends only on the time distance between them. | Pattern Recognition | Stationarity Detection | 85 | [
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] | null |
Are the two time series flipped versions of each other despite noise? | [
"No, they are not flipped versions",
"Yes, they are 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 | 86 | [
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] |
Which additive combination of patterns best describes the time series? | [
"SineWave + SquareWave",
"SawtoothWave + SquareWave",
"SineWave + SawtoothWave"
] | SineWave + SawtoothWave | multiple-choice | 16 | 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 | 87 | [
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] | null |
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? | [
"Square wave with log trend",
"Sawtooth wave with linear trend",
"Sine wave with linear trend"
] | Sine wave with linear trend | multiple_choice | 72 | 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 | 88 | [
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] | null |
The given time series is a sawtooth wave. What is the most likely amplitude of the sawtooth wave? | [
"1.56",
"5.04",
"8.66"
] | 8.66 | 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 | 89 | [
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2.7516958086715984
] | null |
Does any part of the given time series, composed of several concatenated patterns, appear to be stationary? | [
"Yes",
"No"
] | Yes | binary | 33 | 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 | 90 | [
0.34575616026615824,
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] | null |
What type of trend does the time series exhibit in the latter half? | [
"Exponential",
"Linear",
"No trend"
] | Exponential | 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 | 91 | [
-0.23425234997416627,
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-0.010563907933671783,
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1.6417661116150537,
1.6897715482143854,
1.650364515414954,
1.8056168128028107
] | null |
The given time series has multiple trends followed by each other, what is the correct ordering of the trend components? | [
"Log",
"Linear -> Exponential",
"Linear -> Exponential -> Log",
"Exponential -> Linear -> Log"
] | Exponential -> Linear -> 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 | 92 | [
0.8681429839241888,
1.1367572911502934,
1.1652147175157845,
1.2129203812262026,
1.3761582835884134,
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1.2631926773711704,
1.4663785147530524,
1.7879435048633407,
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2.8690794959019392,
3.1525233023749064,
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19.6115007003642,
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19.739886428488468
] | null |
You are given two time series where one is the lagged version of the other. What is the most likely lagging step? | [
"Lagging step is between 30 to 45",
"Lagging step is between 60 to 75",
"Lagging step is between 5 to 20"
] | Lagging step is between 5 to 20 | multiple_choice | 100 | 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"
] | You already know that one time series is the lagged version of the other. Shift the time series by lags proposed in the options and check which one looks the same as the other time series. | Causality Analysis | Granger Causality | 93 | [
-1.291170151953572,
-1.2489607812434083,
-1.0185196766775075,
-0.2724234496832522,
-0.3290137852877824,
-0.27531383575346147,
-0.5560281024777485,
-0.2782354206891091,
-0.4286122880167262,
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-0.20954805162999407,
-1.2626490094654277,
-0.7661154542286449,
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] |
What is the most likely linear trend coefficient of the given time series? | [
"0",
"4.63",
"7.89"
] | 4.63 | 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 | 94 | [
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5.134387845629573,
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] | null |
Is the given time series a white noise process? | [
"No",
"Yes"
] | No | binary | 50 | 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"
] | 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. Another important property is that the noise is uncorrelated over time. Does the time series seem to have these properties? | Noise Understanding | White Noise Recognition | 95 | [
-2.2355603678074623,
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] | null |
The given time series is a sawtooth wave. What is the most likely amplitude of the sawtooth wave? | [
"4.65",
"1.34",
"17.89"
] | 4.65 | multiple-choice | 24 | 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 | 96 | [
-4.650472728356417,
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-4.037852861159436,
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] | null |
What is the most likely mean of the given time series? | [
"-15.2",
"0.15",
"25.53"
] | -15.2 | 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 | 97 | [
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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"
] | No, they have different underlying distribution | binary | 92 | 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. | [
"AutoRegressive Process",
"Moving Average Process"
] | The difference between AR(1) and MA(1) is that AR(1) is a linear combination of past values and white noise, while MA(1) is a linear combination of past white noise values. You should check if the time series exhibit any dependency on the previous values. This could give you a clue about whether the time series is AR(1) or not. Check this for both time series. | Similarity Analysis | Distributional | 98 | [
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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 cutoff | binary | 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. | [
"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 | 99 | [
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] | null |
Is the given time series likely to be stationary after differencing? | [
"No",
"Yes"
] | Yes | binary | 32 | 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"
] | Differencing is a common technique to make a time series stationary. Focus on checking if the trend is removed after differencing. | Pattern Recognition | Stationarity Detection | 100 | [
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