How to Grid Search Triple Exponential Smoothing for Time Series Forecasting in Python
Last Updated on August 28, 2020
Exponential smoothing is a time series forecasting method for univariate data that can be extended to support data with a systematic trend or seasonal component.
It is common practice to use an optimization process to find the model hyperparameters that result in the exponential smoothing model with the best performance for a given time series dataset. This practice applies only to the coefficients used by the model to describe the exponential structure of the level, trend, and seasonality.
It is also possible to automatically optimize other hyperparameters of an exponential smoothing model, such as whether or not to model the trend and seasonal component and if so, whether to model them using an additive or multiplicative method.
In this tutorial, you will discover how to develop a framework for grid searching all of the exponential smoothing model hyperparameters for univariate time series forecasting.
After completing this tutorial, you will know:
- How to develop a framework for grid searching ETS models from scratch using walk-forward validation.
- How to grid search ETS model hyperparameters for daily time series data for female births.
- How to grid search ETS model hyperparameters for monthly time series data
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