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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Standardized code to train and evaluate machine learning models

There are two main ingredients while training a machine learning model – data and the model itself. Therefore, to standardize the pipeline, we defined three configuration classes (FeatureConfig, MissingValueConfig, and ModelConfig) and another wrapper class (MLForecast) over scikit-learn-style estimators (.fit - .predict) to make the process smooth. Let’s look at each of them.

Notebook alert

To follow along with the code, use the 01-Forecasting with ML.ipynb notebook in the chapter08 folder and the code in the src folder.

FeatureConfig

FeatureConfig is a Python dataclass that defines a few key attributes and functions that are necessary while processing the data. For instance, continuous, categorical, and Boolean columns need separate kinds of preprocessing before being fed into the machine learning model. Let’s see what FeatureConfig holds: