How do you forecast time series decomposition?

Decomposition is a forecasting technique that separates or decomposes historical data into different components and uses them to create a forecast that is more accurate than a simple trend line .

This is called detrending . Time series data is often thought of as being comprised of several components: a long-term trend, seasonal variation, and irregular variations.

To forecast a time series using a decomposition model, you calculate the future values for each separate component and then add them back together to obtain a prediction . The challenge then simply becomes finding the best model for each of the components.

Time series decomposition is a technique that splits a time series into several components, each representing an underlying pattern category, trend, seasonality, and noise. In this tutorial, we will show you how to automatically decompose a time series with Python.20 Nis 2021