--- title: "Use the SEASONALLINEARREGRESSION Function to Analyze Seasonal Trends" slug: "seasonal-linear-regression-function" description: "Learn to use SEASONAL.LINEAR.REGRESSION to compute a linear trend with seasonality adjustments in Pigment for time-based data analysis." updated: 2025-05-28T10:26:33Z published: 2025-08-22T12:00:18Z --- > ## Documentation Index > Fetch the complete documentation index at: https://kb.pigment.com/llms.txt > Use this file to discover all available pages before exploring further. # SEASONAL_LINEAR_REGRESSION function ## Description Computes a linear regression by fitting a straight line to the data and taking seasonality into account. ## Syntax `SEASONAL_LINEAR_REGRESSION(Input Block, Seasonality [, Ranking Dimension])` ## Arguments | Argument | Type | Dimensions | Description | | --- | --- | --- | --- | | *Input Block* (required) | Number | Any Dimensions | This is the data source on which the seasonal linear regression is computed, and must be a Metric with data points as an expression of Integer or Number type. This Metric must include the same Dimension that is used in the `Ranking Dimension` argument. | | *Seasonality* (required) | Integer | No Dimension | Length of the seasonality. It must be greater than 1, for example, if you observe a quarterly on a Metric defined by month, the Seasonality length is 3. If you observe a yearly seasonality on a Metric defined by month, the Seasonality length is 12. | | *Ranking Dimension* (optional) | Dimension | Not applicable | This is a Dimension applied to the time series taken in the `Input Block`. This is optional if it’s a datetime Dimension from the calendar. If this is not the case, then this is mandatory. It’s also mandatory if the Metric is defined on several time Dimensions. | ## Returns | Type | Dimensions | | --- | --- | | Number | Dimensions of *Input Block* | With N being the Seasonality length of the serie, the function returns: - Blank for value before the first non blank value. - (*A* * x +*B*) * *SeasonalityFactor*(x) after the first non blank value To compute *SeasonalityFactor*, *A*and *B*, we use the classical decomposition method, called multiplicative decomposition, over historical data. > Notes: > > - Blank observations (in the input Block) between the first non-blank value and the last non-blank values are considered as 0. > - The function requires 2 times the seasonality in terms of datapoint between the first non-blank value and the last non-blank values. ## Examples | Formula | Description | | --- | --- | | `SEASONAL_LINEAR_REGRESSION(Actuals, 4, Quarter)` | Computes a yearly seasonality over a metric defined by quarter. | | `SEASONAL_LINEAR_REGRESSION( Actuals, 12, Month)` | Computes a yearly seasonality over a metric defined by month. | | `SEASONAL_LINEAR_REGRESSION( Actuals, 3, Month)` | Computes a quarterly seasonality over a metric defined by month. | Example using `SEASONAL_LINEAR_REGRESSION(Actuals, 4)`: ![](https://cdn.document360.io/e47cfe35-dc28-40c7-a083-6cf003073d8e/Images/Documentation/86c33967-3e00-4749-bf55-a908058e8482.png) ## Using SEASONAL_LINEAR_REGRESSION as Forecasting Function A common use case for using the SEASONAL_LINEAR_REGRESSION function is to prepare a forecast. It’s a good method when your observation series shows a linear trend****and a seasonality. ## See also Related articles: [FORECAST ETS](/v1/docs/forecast-ets-function) , [FORECAST_LINEAR](/v1/docs/forecast-linear-function) [References: [Multiplicative decomposition](https://otexts.com/fpp3/classical-decomposition.html#multiplicative-decomposition) , [wikipedia](https://en.wikipedia.org/wiki/Decomposition_of_time_series)]