You're reading from Modern Time Series Forecasting with Python

Product type Book

Published in Nov 2022

Publisher Packt

ISBN-13 9781803246802

Pages 552 pages

Edition 1st Edition

Languages

Concepts

Data Science

Author (1):

Manu Joseph

Table of Contents (26) Chapters

Preface

1. Part 1 – Getting Familiar with Time Series

2. Chapter 1: Introducing Time Series

3. Chapter 2: Acquiring and Processing Time Series Data

4. Chapter 3: Analyzing and Visualizing Time Series Data

5. Chapter 4: Setting a Strong Baseline Forecast

6. Part 2 – Machine Learning for Time Series

7. Chapter 5: Time Series Forecasting as Regression

8. Chapter 6: Feature Engineering for Time Series Forecasting

9. Chapter 7: Target Transformations for Time Series Forecasting

10. Chapter 8: Forecasting Time Series with Machine Learning Models

11. Chapter 9: Ensembling and Stacking

12. Chapter 10: Global Forecasting Models

13. Part 3 – Deep Learning for Time Series

14. Chapter 11: Introduction to Deep Learning

15. Chapter 12: Building Blocks of Deep Learning for Time Series

16. Chapter 13: Common Modeling Patterns for Time Series

17. Chapter 14: Attention and Transformers for Time Series

18. Chapter 15: Strategies for Global Deep Learning Forecasting Models

19. Chapter 16: Specialized Deep Learning Architectures for Forecasting

20. Part 4 – Mechanics of Forecasting

21. Chapter 17: Multi-Step Forecasting

22. Chapter 18: Evaluating Forecasts – Forecast Metrics

23. Chapter 19: Evaluating Forecasts – Validation Strategies

24. Index

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25. Other Books You May Enjoy

Introducing Time Series

Welcome to Advanced Time Series Analysis Using 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. 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 and 1930s. But in the current age of data, the time series that are collected by businesses are growing larger and wider by the minute. Combined with an explosion in the quantum of data collected and the renewed interest in ML, the landscape of time series analysis also changed considerably. This book attempts to take you beyond classical statistical methods such as AutoRegressive Integrated Moving Average (ARIMA) and introduce to you the latest techniques from the ML world in time series analysis.

We are going to start with the basics and quickly scale up to more complex topics. In this chapter, we're going to cover the following main topics:

What is a time series?
Data-generating process (DGP)
What can we forecast?
Forecasting terminology and notation

You're reading from Modern Time Series Forecasting with Python

Table of Contents (26) Chapters

Introducing Time Series

Authors (1)

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