Time Series Decomposition is a method of breaking a complex time series into its constituents or components such as trend, seasonality and noise. A time series is a sequence of data points in a series of time. Complex time series can be broken down into their component parts in order to identify the underlying patterns and create a forecast model. Time series decomposition is often used in business analytics, financial forecasting and other forecasting related tasks.
Time series decomposition can be used to identify various components that make up the time series. These components can help identify various patterns and relationships such as underlying trends and seasonality. Trend is a general direction of a time series, seasonality is the tendency of a time series to repeat itself at regular intervals and noise is the random fluctuation of the time series around the trend and seasonality. By decomposing the time series into these various component parts, it is possible to identify and analyze the underlying patterns and use these for forecasts as well as understanding the behavior of the data.
The most common method of decomposing a time series is the additive model, which breaks the time series into four components: trend, seasonality, cyclic and irregular. The additive model assumes that the components of a time series are additive. That is, the time series can be represented as the sum of the components. The additive model is useful in forecasting future trends and can provide insights into the underlying patterns in the data.
Time series decomposition can also be used to identify specific periods of time that exhibit the same pattern. These periods can be used to create more accurate forecasts and build better models. Additionally, time series decomposition can be used to identify outliers and unexpected behaviors in the data.
Time series decomposition is a valuable tool for data analysis and forecasting. By breaking the complex time series into its constituent components, a better understanding of the underlying patterns and relationships can be gained. This can lead to improved forecasting and more accurate models.