# Understanding time series data

The objective of a *time series machine learning* algorithm is to forecast values and effectively plan the use of resources, such as inventories, seasonal-demand equipment allocation, and agriculture production, for example.

As a regression model needs a statistically significant relationship between the variables, a time series model needs autocorrelated data to be useful for a predictive model. In the following figure, we can see that the regression model variables' relationship is tested by statistical methods such as *f-statistics* and *p-value*:

*Figure 3.1* shows the prediction model for four trimesters of years 11 and 12 from air passenger time series data from the past 10 years. To build a useful predictive model, the air passenger data from years 1 to 10 needs to autocorrelate. This means that each value is dependent on prior data. Looking at the...