You're reading from Modern Time Series Forecasting with Python Explore industry-ready time series forecasting using modern machine learning and deep learning

Product type Paperback

Published in Nov 2022

Publisher Packt

ISBN-13 9781803246802

Length 552 pages

Edition 1st Edition

Languages

Python

Tools

Time Series Forecasting

Concepts

Data Science

Author (1):

Manu Joseph

View More author details

Table of Contents (26) Chapters

Preface

1. Part 1 – Getting Familiar with Time Series

2. Chapter 1: Introducing Time Series FREE CHAPTER

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

Why subscribe?

25. Other Books You May Enjoy

Why Global Forecasting Models (GFMs)?

We talked about global models briefly in Chapter 5, Time Series Forecasting as Regression, where we mentioned related datasets. We can think of many scenarios where we would encounter related time series. We may need to forecast the sales for all the products of a retailer, the number of rides requested for a cab service across different areas of a city, or the energy consumption of all the households in a particular area (which is what the London Smart Meters dataset does). We call these related time series because all the different time series in the dataset can have a lot of factors in common with each other. For instance, the yearly seasonality that might occur in retail products might be present for a large section of products, or the way an external factor such as temperature affects energy consumption may be similar for a large number of households. Therefore, one way or the other, the different time series in a related time series dataset...