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Special Topics in TimeGPT

TimeGPT is a robust foundation model for time series forecasting. It provides advanced capabilities, including hierarchical and bounded forecasts. Certain special situations require specific considerations, such as handling irregular timestamps or datasets containing missing values, to leverage the full potential of TimeGPT.

In this section, we cover these special topics to help you get the most out of TimeGPT:

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Overview of Special Topics

Irregular Timestamps

Learn how to manage irregular timestamps effectively to ensure correct utilization of TimeGPT.

Bounded Forecasts

Explore how to generate forecasts within defined limits using TimeGPT, ideal for bounded-outcome scenarios.

Hierarchical Forecasts

Understand how to perform coherent forecasts at multiple aggregation levels using TimeGPT.

Missing Values

Learn effective strategies for handling missing data points in time series when using TimeGPT.

Improve Forecast Accuracy

Discover techniques to enhance forecasting accuracy when working with TimeGPT.

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Getting Started with Special Topics

Sometimes, the best way to integrate special features in TimeGPT is by following a series of clear, sequential steps. Below is a simplified workflow to guide you:

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Step 1: Identify the Special Topic

Determine the challenge you are addressing (e.g., irregular timestamps, bounded forecasts, hierarchical forecasts, handling missing values, or improving accuracy).

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Step 2: Prepare Your Data

Align your time series data with the requirements of the specific topic.

For instance, if timestamps are irregular, you might need to resample or align data before passing it to TimeGPT.

3

Step 3: Configure TimeGPT

Modify your forecasts to accommodate the special topic. For example, set upper and lower bounds for bounded forecasts.

# Example: Configuring TimeGPT for bounded forecasting
from timegpt import TimeGPT

timegpt_model = TimeGPT(
    lower_bound=0, 
    upper_bound=100  # Example bounds
)

# Fit the model (pseudo-code)
timegpt_model.fit(training_data)

# Make a forecast with the specified bounds
forecast = timegpt_model.predict(future_data)
print(forecast)
4

Step 4: Monitor and Evaluate Forecasts

Use appropriate evaluation metrics to ensure the forecasts meet your accuracy requirements. Adjust parameters or data preprocessing steps as needed.

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Step 5: Iterate and Improve

Incorporate feedback from real-world usage to refine your approach. Revisit the documentation for each specific topic and apply best practices.

With a careful approach to preparing data and configuring TimeGPT for these special scenarios, you can unlock superior forecasting performance for a wide range of real-world applications.