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

Using the scale of the time series

We used GroupNormlizer in TimeSeriesDataset to scale each household using its own mean and standard deviation. We did this because we wanted to make the target zero mean and unit variance so that the model does not waste effort trying to change its parameters to capture the scale of individual household consumption. Although this is a good strategy, we do have some information loss here. There may be patterns that are specific to households whose consumption is on the larger side and some other patterns that are specific to households that consume much less. But now, they are both lumped in together and the model tries to learn common patterns. In such a scenario, these unique patterns seem like noise to the model because there is no variable to explain those.

The bottom line is that there is information in the scale that we removed, and adding that information back would be beneficial. So, how do we add it back? Definitely not by including the...

The rest of the chapter is locked

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

Table of Contents (26) Chapters

Using the scale of the time series

Authors (1)

Personalised recommendations for you

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

Table of Contents (26) Chapters

Using the scale of the time series

Unlock this book and the full library FREE for 7 days

Authors (1)

Personalised recommendations for you