Page 166 - Data Science Algorithms in a Week

7

Time Series Analysis

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