AutoSeasonality

Automatically identifies and quantifies optimal seasonality in time series data to improve forecasting model performance.

Purpose: The AutoSeasonality metric’s purpose is to automatically detect and identify the best seasonal order or period for each variable in a time series dataset. This detection helps to quantify periodic patterns and seasonality that reoccur at fixed intervals of time in the data. This is especially significant for forecasting-based models, where understanding the seasonality component can drastically improve prediction accuracy.

Test Mechanism: This metric uses the seasonal decomposition method from the Statsmodels Python library. The function takes the additive’ model type for each variable and applies it within the prescribed range of ‘min_period’ and max_period’. The function decomposes the seasonality for each period in the range and calculates the mean residual error for each period. The seasonal period that results in the minimum residuals is marked as the ‘Best Period’. The test results include the ‘Best Period’, the calculated residual errors, and a determination of ‘Seasonality’ or No Seasonality’.

Signs of High Risk:

• If the optimal seasonal period (or ‘Best Period’) is consistently at the maximum or minimum limit of the offered range for a majority of variables, it may suggest that the range set does not adequately capture the true seasonal pattern in the series.
• A high average ‘Residual Error’ for the selected ‘Best Period’ could indicate issues with the model’s performance.

Strengths:

• The metric offers an automatic approach to identifying and quantifying the optimal seasonality, providing a robust method for analyzing time series datasets.
• It is applicable to multiple variables in a dataset, providing a comprehensive evaluation of each variable’s seasonality.
• The use of concrete and measurable statistical methods improves the objectivity and reproducibility of the model.

Limitations:

• This AutoSeasonality metric may not be suitable if the time series data exhibits random walk behaviour or lacks clear seasonality, as the seasonal decomposition model may not be appropriate.
• The defined range for the seasonal period (min_period and max_period) can influence the outcomes. If the actual seasonality period lies outside this range, this method will not be able to identify the true seasonal order.
• This metric may not be able to fully interpret complex patterns that go beyond the simple additive model for seasonal decomposition.
• The tool may incorrectly infer seasonality if random fluctuations in the data match the predefined seasonal period range.