Perspective Chapter: An Overview of Time Series Decomposition and Its Applications
Pankaj Kumar Das, Samir Barman · Business, management and economics · 2025
Time series (TS) data is ubiquitous in various fields such as finance, economics, meteorology, and engineering. The analysis of TS data aims to understand the underlying patterns, make predictions, and inform decision-making. One of the fundamental techniques in TS analysis is decomposition, which breaks down a TS into its constituent components: trend, seasonality, and residuals. This chapter provides a comprehensive overview of TS decomposition, breaking down data into trend, seasonality, and residuals. It covers classical methods, such as additive and multiplicative models, advanced techniques like X-12-ARIMA and Seasonal-Trend decomposition using LOESS (STL), and recent approaches, including machine learning (ML) based decompositions. Practical applications in agriculture, meteorology, and economics, along with challenges like non-stationarity and nonlinear behavior, are discussed. The chapter offers guidelines for selecting appropriate methods and includes case studies for real-world insights. It is a valuable resource for researchers, data scientists, and professionals analyzing complex TS data.