Mankind has always sought the ability to predict the future. Since the earliest civilizations, people have tried to predict the future. Shamans, oracles, and prophets used anything ranging from astrology and palmistry to numerology to satisfy the human need to see into the future. In the last century, with the developments in IT, the mantle of predicting the future landed on data analysts and data scientists. And how do we predict the future? It’s not by examining the lines and creases on our hands or the positions of the stars anymore but by using data that has been generated in the past. And instead of prophecies, we now have forecasts.

Time, being the fourth dimension in our world, makes all the data generated in the world time series data. All the data that is generated in the real world has an element of time associated with it. Whether the temporal aspect is relevant to the problem or not is another question altogether. However, to be more concrete and immediate, we can find time series forecasting use cases in many industries, such as retail, energy, healthcare, and finance. We might want to know how many units of a particular product are to be dispatched to a particular store, or we might want to know how much electricity is to be produced to meet demand.

In this book, using a real-world dataset, you will learn how to handle and visualize time series data using pandas and plotly, generate baseline forecasts using darts, and use machine learning and deep learning for forecasting, using popular Python libraries such as scikit-learn and PyTorch. We conclude the book with a few chapters that cover seldom-touched aspects, such as multi-step forecasting, forecast metrics and cross validation for time series.

The book will enable you to build real-world time series forecasting systems that scale to millions of time series by mastering and applying modern concepts in machine learning and deep learning.