Decomposition of time series

Overview

Decomposition of time series is the process of decomposing time-series data into constituent parts. Time-series data are series of data observed over time and may be composed of factors such as trend, seasonality, periodicity, and noise.

Components of a time series

• Trend: This component shows long-term trend or fluctuations in data. Trend shows the general pattern of data, such as whether the data is trending upwards or downwards, or whether it has a flat trend.
• Seasonality: This component's data are repeated at regular intervals. Seasonality describes the patterns and variations in data during specific seasons or times of the year. For example, a company's sales might increase at the same time each year.
• Cyclical: This component's data fluctuates in a long-term cycle. Cyclical shows a longer-term variation pattern than seasonality. For example, economic cycles and business cycles are examples of cyclical.
• Noise (or "irregular"): This component consists of random or unpredictable variation that remains in data. Noise can be considered the residual portion of the data after it has been decomposed into other components.

Importance of time series decomposition

Time series decomposition allows us to understand the characteristics of data and analyze components such as trend, seasonality, and cyclical separately. This allows us to understand patterns and trend in data and helps analyze and forecast time series.

Technique for time series decomposition

Techniques and algorithms such as moving averages, low-pass filters, Holt-Winter methods, and ARIMA models are used to decompose time series. These methods are used to extract components such as trend and seasonality.