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Jun 30, 2025
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Update "Historical data for training" sections within Predict docs #21047

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Update historical data for training in Predict docs
amohammed-2 Jun 27, 2025
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Update src/content/docs/nrql/nrql-syntax-clauses-functions.mdx
amohammed-2 Jun 30, 2025
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Update src/content/docs/query-your-data/explore-query-data/use-charts…
amohammed-2 Jun 30, 2025
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2 changes: 1 addition & 1 deletion src/content/docs/nrql/nrql-syntax-clauses-functions.mdx
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Expand Up @@ -849,7 +849,7 @@ Here're some examples:

Prediction window: Predicts a range equal to 20% of the total time span specified in your query window.

Historical data for training: Uses historical data from the current query window and the two preceding ones to generate predictions.
Historical data for training: Uses historical data from the last three query windows (the current one and the two preceding it). However, if you provide a `seasonality` hyperparameter that is greater than the query window, the model instead uses historical data spanning a period equal to three times the seasonality.

Time interval: Aligns with the data point interval of the time series in the query window to ensure consistency in data projection.
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2 changes: 1 addition & 1 deletion ...content/docs/query-your-data/explore-query-data/use-charts/nrql-predictions.mdx
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Expand Up @@ -102,7 +102,7 @@ The `PREDICT` clause in a query comes with the following default behaviors:
* **Seasonality**: Automatically detects whether seasonality is present in the historic data. If seasonality is detected, the identified season length is used in the Holt-Winters seasonal algorithm. If no seasonality is found, it constructs a non-seasonal model.
* **Hyperparameters**: Sets the hyperparameters for the Holt-Winters algorithm based upon the seasonality and historical data.
* **Prediction window**: Predicts a range equal to 20% of the total time span specified in your query window.
* **Historical data for training**: Uses historical data from the current query window and the two preceding ones to generate predictions.
* **Historical data for training**: Uses historical data from the last three query windows (the current one and the two preceding it). However, if you provide a `seasonality` hyperparameter that is greater than the query window, the model instead uses historical data spanning a period equal to three times the seasonality.
* **Time interval**: Aligns with the data point interval of the time series in the query window to ensure consistency in data projection.

## Customizing the predictive model [#customize-prediction-range]
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