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Welcome to Modern Time Series Forecasting with Python! This book is intended for data scientists or machine learning (ML) engineers who want to level up their time series analysis skills by learning new and advanced techniques from the ML world. Time series analysis is something that is commonly overlooked in regular ML books, courses, and so on. They typically start with classification, touch upon regression, and then move on. But it is also something that is immensely valuable and ubiquitous in business. We look at the world in a three-dimensional perspective. But time is the hidden dimension which we rarely think about, but is all-pervasive. And as long as time is one of the four dimensions in the world we live in, time series data is all-pervasive.

Analyzing time series data unlocks a lot of value for a business. Time series analysis isn't new—it's been around since the 1920s. But in the current...

Technical requirements

You will need to set up the Anaconda environment following the instructions in the Preface of the book to get a working environment with all the libraries and datasets required for the code in this book. Any additional library will be installed while running the notebooks.

The associated code for the chapter can be found at https://github.com/PacktPublishing/Modern-Time-Series-Forecasting-with-Python-/tree/main/notebooks/Chapter01.

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.

Data-generating process (DGP)

We saw that time series data is a collection of observations made sequentially along the time dimension. Any time series is, in turn, generated by some kind of mechanism. For example, time series data of daily shipments of your favorite chocolate from the manufacturing plant is affected by a lot of factors such as the time of the year, the holiday season, the availability of cocoa, the uptime of the machines working on the plant, and so on. In statistics, this underlying process that generated the time series is referred to as the DGP. Time series data is produced by stochastic and deterministic process. The deterministic processes involve quantities that evolve in a predictable manner over time. An example of this is the radioactive decay of an element, where the remaining quantity diminishes according to a precise mathematical formula, leading to a consistent reduction over time. But most of the interesting time series (from a forecasting perspective) are...

What can we forecast?

Before we move ahead, there is another aspect of time series forecasting that we have to understand—the predictability of a time series. The most basic assumption when we forecast a time series is that the future depends on the past. But not all time series are equally predictable.

Let's take a look at a few examples and try to rank these in order of predictability (from easiest to hardest), as follows:

• High tide next Monday
• Lottery numbers next Sunday
• The stock price of Tesla next Friday

Intuitively, it is very easy for us to rank them. High tide next Monday is going to be the easiest to predict because it is so predictable, the lottery numbers are going to be very hard to predict because these are pretty much random, and the stock price of Tesla next Friday is going to be difficult to predict, but not impossible.

NOTE

However, for people thinking that they can forecast stock prices with the advanced techniques covered in the book and get rich, that...

Forecasting terminology

There are a few terminologies that will help you follow the book as well as other literature on time series. These are described in more detail here:

• Forecasting

Forecasting is the prediction of future values of a time series using the known past values of the time series and/or some other related variables. This is very similar to prediction in ML where we use a model to predict unseen data.

• Multivariate forecasting

Multivariate time series consist of more than one time series variable that is not only dependent on its past values but also has some dependency on the other variables. For example, a set of macroeconomic indicators such as gross domestic product (GDP), inflation, and so on of a particular country can be considered as a multivariate time series. The aim of multivariate forecasting is to come up with a model that captures the interrelationship between the different variables along with its relationship with its past and forecast all the time series...

Summary

We had our first dip into time series as we understood the different types of time series, looked at how a DGP generates a time series, and saw how we can think about the important question: How well can we forecast a time series? We also had a quick review of the terminology and notation required to understand the rest of the book. In the next chapter, we will be getting our hands dirty and will learn how to work with time series data, how to preprocess a time series, how to handle missing data and outliers, and so on. If you have not set up the environment yet, take a break and put some time into doing that.