What is a time series?

To keep it simple, a time series is a set of observations taken sequentially in time. The focus is on the word time. If we keep taking the same observation at different points in time, we will get a time series. For example, if you keep recording the number of bars of chocolate you have in a month, you'll end up with a time series of your chocolate consumption. Suppose you are recording your weight at the beginning of every month. You get another time series of your weight. Is there any relation between the two time series? Most likely, yeah. But we can analyze that scientifically by the end of this book.

A few other examples of time series are the weekly closing price of a stock that you follow, daily rainfall or snow in your city, or hourly readings of your heartbeat from your smartwatch.

Types of time series

There are two types of time series data, as outlined here:

• Regular time series: This is the most common type of time series where we have observations coming in at regular intervals of time, such as every hour or every month.
• Irregular time series: There are a few time series where we do not have observations at a regular interval of time. For example, consider we have a sequence of readings from lab tests of a patient. We see an observation in the time series only when the patient heads to the clinic and carries out the lab test, and this may not happen in regular intervals of time.

Important note

This book only focuses on regular time series, which are evenly spaced in time. Irregular time series are slightly more advanced and require specialized techniques to handle them. A couple of survey papers on the topic is a good way to get started on irregular time series and you can find them in the Further reading section of this chapter.

Main areas of application for time series analysis

There are broadly three important areas of application for time series analysis, outlined as follows:

• Time series forecasting: Predicting the future values of a time series, given the past values—for example, predict the next day's temperature using the last 5 years of temperature data.
• Time series classification: Sometimes, instead of predicting the future value of the time series, we may also want to predict an action based on past values. For example, given a history of an electroencephalogram (EEG; tracking electrical activity in the brain) or an electrocardiogram (EKG; tracking electrical activity in the heart), we need to predict whether the result of an EEG or an EKG is normal or abnormal.
• Interpretation and causality: Understand the whats and whys of the time series based on the past values, understand the interrelationships among several related time series, or derive causal inference based on time series data.

Important note

The focus of this book is predominantly on time series forecasting, but the techniques that you learn will help you approach time series classification problems also, with minimal change in the approach. Interpretation is also addressed, although only briefly, but causality is an area that this book does not address because it warrants a whole different approach.

Now that we have an overview of the time series landscape, let's build a mental model on how time series data is generated.