A Gentle Introduction to Exponential Smoothing for Time Series Forecasting in Python
Last Updated on April 12, 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 a powerful forecasting method that may be used as an alternative to the popular Box-Jenkins ARIMA family of methods.
In this tutorial, you will discover the exponential smoothing method for univariate time series forecasting.
After completing this tutorial, you will know:
- What exponential smoothing is and how it is different from other forecasting methods.
- The three main types of exponential smoothing and how to configure them.
- How to implement exponential smoothing in Python.
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A Gentle Introduction to Exponential Smoothing for Time Series Forecasting in Python
Photo by Wolfgang Staudt, some rights reserved.
Tutorial Overview
This tutorial is divided into 4 parts; they are: