What Is Time Series Decomposition? A Detailed Introduction

Time series decomposition is a statistical process used by businesses to break down time series datasets into individual components such as trend, seasonality, and noise. This technique helps analysts discover patterns and variations within the data, making it easier to model and forecast future data points, identify anomalies, and make accurate data-driven decisions. Time series decomposition is applicable in various fields like economics, retail, healthcare, manufacturing, and logistics. Two common methods for decomposition are the additive model and the multiplicative model. Python, R, Julia, and Rust are some programming languages used for time series decomposition.