Descriptive time series analysis

The goal of descriptive time series analysis is to understand time-ordered data (time series) by visually uncovering patterns, trends, and seasonality—as well as using simple metrics (such as rates of change)—to identify the underlying structure (trend, seasonality, cyclicality, and randomness) and serve as a foundation for more complex forecasting models (such as ARIMA). It decomposes the time series into its constituent components in order to describe historical behavior.