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

Setting a forecast horizon

Although we generated forecasts earlier in this book, we never explicitly discussed forecast horizons. A forecast horizon is the number of time steps into the future we want to forecast at any point in time. For instance, if we want to forecast the next 24 hours for the electricity consumption dataset that we have been working with, the forecast horizon becomes 48 (because the data is half-hourly). In Chapter 5, Time Series Forecasting as Regression, where we generated baselines, we just predicted the entire test data at once. In such cases, the forecast horizon becomes equal to the length of the test data.

We never had to worry about this until now because, in the classical statistical methods of forecasting, this decision is decoupled from modeling. If we train a model, we can use that model to predict any future point without retraining. But with time series forecasting as regression, we have a constraint on the forecast horizon and it has its roots...