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TimeGPT can help you understand both a forecast and the historical information behind it. Start with the question you are trying to answer.

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What would you like to understand?

Why is this forecast high or low?

Break one forecast into a starting value and the contribution from each input.

What is the forecast sensitive to?

See how the forecast changes when one input is replaced by its typical historical value.

Which signals have been useful historically?

Rank the features that have been most useful for predicting your target over time.

I need more control

Compare relationship analyses, history-weighted forecast allocations, and stability across data windows.
If you have a forecast in front of you and are unsure where to start, choose Explain a forecast with SHAP.

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One example, three questions

The guides use the same retail example throughout: a store forecasts daily demand using price, promotion, and temperature.
A retail-demand forecast beside a bar chart showing price, promotion, and temperature contributions

An actual TimeGPT 2.1 forecast and its feature contributions for one promotion day.

Each guide looks at this example from a different angle:

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A simple rule

  • Use SHAP to explain a forecast you already made.
  • Use intervention to explore the forecast’s sensitivity.
  • Use historical signals before forecasting or when reviewing your data.
Explanations are a practical way to investigate forecasts and prioritize follow-up work. For a major business change, combine them with your normal testing and decision process.

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Next

Start with Explain a forecast with SHAP, or go directly to Test forecast sensitivity.