You're reading from Modern Time Series Forecasting with Python Industry-ready machine learning and deep learning time series analysis with PyTorch and pandas

Product type Paperback

Published in Oct 2024

Publisher Packt

ISBN-13 9781835883181

Length 660 pages

Edition 2nd Edition

Languages

Python

Tools

PyTorch

Concepts

Data Science

Authors (2):

Manu Joseph

Jeffrey Tackes

View More author details

Table of Contents (27) Chapters

Preface

1. Part 1: Getting Familiar with Time Series FREE CHAPTER

2. Introducing Time Series

3. Acquiring and Processing Time Series Data

4. Analyzing and Visualizing Time Series Data

5. Setting a Strong Baseline Forecast

6. Part 2: Machine Learning for Time Series

7. Time Series Forecasting as Regression

8. Feature Engineering for Time Series Forecasting

9. Target Transformations for Time Series Forecasting

10. Forecasting Time Series with Machine Learning Models

11. Ensembling and Stacking

12. Global Forecasting Models

13. Part 3: Deep Learning for Time Series

14. Introduction to Deep Learning

15. Building Blocks of Deep Learning for Time Series

16. Common Modeling Patterns for Time Series

17. Attention and Transformers for Time Series

18. Strategies for Global Deep Learning Forecasting Models

19. Specialized Deep Learning Architectures for Forecasting

20. Probabilistic Forecasting and More

21. Part 4: Mechanics of Forecasting

22. Multi-Step Forecasting

23. Evaluating Forecast Errors—A Survey of Forecast Metrics

24. Evaluating Forecasts—Validation Strategies

25. Other Books You May Enjoy

26. Index

Index

absolute error

Geometric Mean Absolute Error 570

Mean Absolute Error (MAE) 570

Median Absolute Error 570

Weighted Mean Absolute Error 570

activation functions 284

hyperbolic tangent (tanh) 286

rectified linear units (ReLUs) 287

sigmoid 285

Adaptive Conformal Inference (ACI) 522, 538

Add and Norm block 450

Aggregate-Disaggregate Intermittent Demand Approach (ADIDA) 539

aggregate metrics 208, 568

Akaike Information Criterion (AIC) 98

Aleatoric Uncertainty 473

algorithmic partitioning 260-264

alignment functions 360

additive/concat attention 362, 363

dot product 360, 361

general attention 362

scaled dot product attention 361

Anaconda environment 147

attention 355-358

Bahdanau, versus Luong 366

forecasting 364-367

Augmented Dickey-Fuller (ADF) test 152

AutoARIMA 89

autocorrelation 88

Auto-Correlation block 434

autocorrelation function (ACF) 161

auto-correlation...

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You're reading from Modern Time Series Forecasting with Python Industry-ready machine learning and deep learning time series analysis with PyTorch and pandas

Table of Contents (27) Chapters

Index

Authors (2)

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