Time series statistics and statistical forecasting

Data exploration and statistical analysis are crucial steps in understanding the characteristics of time series data. In this section, we’ll walk you through how to perform data exploration and apply statistical analysis techniques in Python to gain valuable insights into your time series.

Statistical analysis for time series data

After exploring the data using the plots in the previous section, let’s move on to statistical analysis to gain a deeper understanding. This section focuses on two areas:

• The Augmented Dickey-Fuller (ADF) test: This statistical test is used to determine whether the time series data is stationary. Stationary data is easier to model and forecast.
• Time series decomposition: Time series decomposition separates the data into its constituent components: trend, seasonality, and residuals. This decomposition aids in isolating patterns for forecasting.

We’ll understand...