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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Index

As this ebook edition doesn't have fixed pagination, the page numbers below are hyperlinked for reference only, based on the printed edition of this book.

Symbols

.dt accessor

using 25, 26

absolute error (AE) 471, 472

loss curves and complementary pairs for 478

Acorn classes 21

activation functions 273

Hyperbolic tangent (tanh) 275

sigmoid 274

Add and Norm block 438

additive attention 354, 355

Air Quality Monitoring Data

reference link 29

algorithmic partitioning 251-255

alignment function 350, 352, 425

additive/concat attention 354, 355

dot product 352

general attention 353, 354

scaled dot product attention 353

attention 348-350

forecasting with 356-360

attention distillation 427

Augmented Dickey-Fuller (ADF) test 143, 144

Auto ARIMA 88

autocorrelation 87

autocorrelation function (ACF) 152

auto-correlation mechanism, Autoformer model 433

period-based dependencies...

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Table of Contents (26) Chapters

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Authors (1)

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Table of Contents (26) Chapters

Index

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