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

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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

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Training and predicting for multiple households

We have picked a few models (LassoCV, XGBRFRegressor, and LGBMRegressor) that are doing better in terms of metrics, as well as runtime, to run on all the selected households in our validation dataset. The process is straightforward: loop over all the unique combinations, inner loop over the different models to run, and then train, predict, and evaluate. The code is available in the 01-Forecasting with ML.ipynb notebook in chapter08, under the Running an ML Forecast For All Consumers heading. You can run the code and take a break because this is going to take a little less than an hour. The notebook also calculates the metrics and contains a summary table that will be ready for you when you’re back. Let’s look at the summary now:

Figure 8.19 – Aggregate metrics on all the households in the validation dataset

Here, we can see that even at the aggregated level, the different models we used perform...