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

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Avoiding data leakage

Data leakage occurs when the model is trained with some information that would not be available at the time of prediction. Typically, this leads to high performance in the training set, but very poor performance in unseen data. There are two types of data leakage:

Target leakage is when the information about the target (that we are trying to predict) leaks into some of the features in the model, leading to an overreliance of the model on those features, ultimately leading to poor generalization. This includes features that use the target in any way.
Train-test contamination is when there is some information leaking between the train and test datasets. This can happen because of careless handling and splitting of data. But it can also happen in more subtle ways, such as scaling the dataset before splitting the train and test sets.

When we are working with time series forecasting problems, the biggest and most common mistake that we can make...