Decomposing time series data
When conducting time series analysis, one key objective often involves forecasting, where you build a model capable of making future predictions. Before starting the modeling process, it is crucial to extract the components of the time series for analysis. This step is essential for making informed decisions throughout the modeling process.
A time series typically comprises of three main components: trend, seasonality, and the residual random process. For statistical models that require the time series to be stationary, estimating and subsequently removing the trend and seasonality components from the time series might be necessary. Techniques and libraries for time series decomposition generally provide visual representations and identification of the trend, seasonality, and the residual random process.
The trend component reflects the long-term direction of the time series, which can be upward, downward, or horizontal. For instance, a sales data time series...
