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Modern Time Series Forecasting with Python

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

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Product type Paperback
Published in Nov 2022
Publisher Packt
ISBN-13 9781803246802
Length 552 pages
Edition 1st Edition
Languages
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Author (1):
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Manu Joseph Manu Joseph
Author Profile Icon Manu Joseph
Manu Joseph
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Toc

Table of Contents (26) Chapters Close

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 25. Other Books You May Enjoy

Conventions used

There are a number of text conventions used throughout this book.

Code in text: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: “statsmodels.tsa.seasonal has a function called seasonal_decompose.”

A block of code is set as follows:

#Does not support missing values, so using imputed ts instead
res = seasonal_decompose(ts, period=7*48, model="additive", extrapolate_trend="freq")

Any command-line input or output is written as follows:

conda env create -f anaconda_env.yml

Bold: Indicates a new term, an important word, or words that you see onscreen. For instance, words in menus or dialog boxes appear in bold. Here is an example: “But if you look at the Time Elapsed column, it stands out.

Tips or important notes

Appear like this.

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