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:

• The trend of the data: does the variable tend to rise or fall as time passes? For example, does human population grow or shrink?
• Seasonality: how is the data dependent on certain regular events in time? For example, are restaurant sales bigger 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 chapter, you will learn the following:

• How to analyse data trends using regression in an example business profits
• How to observe and analyse recurring patterns in data in a form of seasonality in an example about an Electronics shop's sales
• Using the example of an electronics shop's sales, to combine the analysis of trends and seasonality to predict time-dependent data
• Create time-dependent models in R using the examples of business profits and an electronics shop's sales