Explain decomposition as time series analysis technique?

Decomposition is a time series analysis technique that involves breaking down a time series data into its individual components, such as trend, seasonality, and random fluctuations.

The decomposition process helps in understanding the underlying patterns and structures within the time series data, which can be useful for forecasting and making informed decisions.

There are several methods for decomposing a time series, including additive decomposition and multiplicative decomposition. In additive decomposition, the time series is broken down into the sum of its components, while in multiplicative decomposition, the time series is broken down into the product of its components.

Overall, decomposition is a powerful technique for analyzing time series data and extracting valuable insights that can be used for forecasting and decision-making.