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import streamlit as st
import pandas as pd
import torch
from chronos import ChronosPipeline
import matplotlib.pyplot as plt
import numpy as np
# Load the Chronos Pipeline model
@st.cache_resource
def load_pipeline():
pipeline = ChronosPipeline.from_pretrained(
"amazon/chronos-t5-small",
device_map="cpu", # Change to CPU
torch_dtype=torch.float32, # Use float32 for CPU
)
return pipeline
pipeline = load_pipeline()
# Streamlit app interface
st.title("Time Series Forecasting Demo with Deep Learning models")
st.write("This demo uses the ChronosPipeline model for time series forecasting.")
# Default time series data (comma-separated)
default_data = """
112, 118, 132, 129, 121, 135, 148, 148, 136, 119, 104, 118, 115, 126, 141, 135, 125, 149, 170, 170, 158,
133, 114, 140, 145, 150, 178, 163, 172, 178, 199, 199, 184, 162, 146, 166, 171, 180, 193, 181, 183, 218,
230, 242, 209, 191, 172, 194, 196, 196, 236, 235, 229, 243, 264, 272, 237, 211, 180, 201, 204, 188, 235,
227, 234, 264, 302, 293, 259, 229, 203, 229, 242, 233, 267, 269, 270, 315, 364, 347, 312, 274, 237, 278,
284, 277, 317, 313, 318, 374, 413, 405, 355, 306, 271, 306, 315, 301, 356, 348, 355, 422, 465, 467, 404,
347, 305, 336, 340, 318, 362, 348, 363, 435, 491, 505, 404, 359, 310, 337, 360, 342, 406, 396, 420, 472,
548, 559, 463, 407, 362, 405, 417, 391, 419, 461, 472, 535, 622, 606, 508, 461, 390, 432
"""
# Input field for user-provided data
user_input = st.text_area(
"Enter time series data (comma-separated values):",
default_data.strip()
)
# Convert user input into a list of numbers
def process_input(input_str):
return [float(x.strip()) for x in input_str.split(",")]
try:
time_series_data = process_input(user_input)
except ValueError:
st.error("Please make sure all values are numbers, separated by commas.")
time_series_data = [] # Set empty data on error to prevent further processing
# Select the number of months for forecasting
prediction_length = st.slider("Select Forecast Horizon (Months)", min_value=1, max_value=64, value=12)
# If data is valid, perform the forecast
if time_series_data:
# Convert the data to a tensor
context = torch.tensor(time_series_data, dtype=torch.float32)
# Make the forecast
forecast = pipeline.predict(
context=context,
prediction_length=prediction_length,
num_samples=20,
)
# Prepare forecast data for plotting
forecast_index = range(len(time_series_data), len(time_series_data) + prediction_length)
low, median, high = np.quantile(forecast[0].numpy(), [0.1, 0.5, 0.9], axis=0)
# Plot the historical and forecasted data
plt.figure(figsize=(8, 4))
plt.plot(time_series_data, color="royalblue", label="Historical data")
plt.plot(forecast_index, median, color="tomato", label="Median forecast")
plt.fill_between(forecast_index, low, high, color="tomato", alpha=0.3, label="80% prediction interval")
plt.legend()
plt.grid()
# Show the plot in the Streamlit app
st.pyplot(plt)
# Note for comments, feedback, or questions
st.write("### Notes")
st.write("For comments, feedback, or any questions, please reach out to me on [LinkedIn](https://www.linkedin.com/in/mjdarvishi/).") |