Benefits of Exponential Smoothing

Analysts can modify the rate at which older observations become less significant in the computations by varying the values of these parameters. As a result, analysts can adjust the weighting of recent observations in relation to previous observations to suit the needs of their field.

On the other hand, the moving average approach assigns 0 weight to observations outside of the moving average window and assigns equal weight to all historical observations when they occur within its frame. Because exponential smoothing models error, trend, and seasonality in time series data, statisticians refer to it as an ETS model, just like they do with the Box-Jenkins ARIMA methodology.

Exponential Smoothing for Time Series Forecasting

Exponential smoothing is a popular time series forecasting method known for its simplicity and accuracy in predicting future trends based on historical data. It assumes that future patterns will be similar to recent past data and focuses on learning the average demand level over time. In this article, we will make a detailed analysis of the Exponential Smoothing for Time Series Forecasting.

Table of Content

Exponential Smoothing Forecasting
Types of Exponential Smoothing

1. Simple or Single Exponential smoothing
2. Double Exponential Smoothing
3. Holt-Winters’ exponential smoothing

Exponential smoothing in Python

The dataset
Setting up the environment
Loading the data
Single Exponential smoothing
Double Exponential Smoothing
Holt-Winter’s Seasonal Smoothing