Time series analysis is the analysis of time-dependent data. Given data for a certain period, the aim is to predict data for a different period, usually in the future. For example, time series analysis is used to predict financial markets, earthquakes, and weather.
In this chapter, we are mostly concerned with predicting the numerical values of certain quantities, for example, the human population in 2030.
The main elements of time-based prediction are as follows:
- Trends: Does the variable tend to rise or fall as time passes? For example, does the human population grow or shrink?
- Seasonality: How is the data dependent on certain regular events in time? For example, are restaurant profits greater on Fridays than on Tuesdays?
Combining these two elements of time series analysis equips us with a powerful method to make time-dependent predictions.
In this...
