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Modern Time Series Forecasting with Python

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 658 pages

Edition 2nd Edition

Languages

Python

Tools

PyTorch

Concepts

Data Science

Authors (2):

Jeffrey Tackes

Manu Joseph

View More author details

Table of Contents (26) 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. Index

Index

A

absolute error

Geometric Mean Absolute Error 566

Mean Absolute Error (MAE) 566

Median Absolute Error 566

Weighted Mean Absolute Error 566

activation functions 282

hyperbolic tangent (tanh) 283

rectified linear units (ReLUs) 284

sigmoid 283

Adaptive Conformal Inference (ACI) 518, 534

Add and Norm block 446

Aggregate-Disaggregate Intermittent Demand Approach (ADIDA) 535

aggregate metrics 206, 564

Akaike Information Criterion (AIC) 97

Aleatoric Uncertainty 469

algorithmic partitioning 258-262

alignment functions 356

additive/concat attention 358, 359

dot product 356, 357

general attention 358

scaled dot product attention 357

Anaconda environment 145

attention 351-354

Bahdanau, versus Luong 362

forecasting 360-363

Augmented Dickey-Fuller (ADF) test 149

AutoARIMA 88

autocorrelation 87

Auto-Correlation block 430

autocorrelation function (ACF) 158

auto-correlation...

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Authors (2)

Manu Joseph

Manu Joseph

Manu Joseph is a self-made data scientist with more than a decade of experience working with many Fortune 500 companies enabling digital and AI transformations, specifically in machine learning-based demand forecasting. He is considered an expert, thought leader, and strong voice in the world of time series forecasting. Currently, Manu leads applied research at Thoucentric, where he advances research by bringing cutting-edge AI technologies to the industry. He is also an active open-source contributor and developed an open-source library—PyTorch Tabular—which makes deep learning for tabular data easy and accessible. Originally from Thiruvananthapuram, India, Manu currently resides in Bengaluru, India, with his wife and son

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Jeffrey Tackes

Jeffrey Tackes

Jeff Tackes is a seasoned data scientist specializing in demand forecasting with over a decade of industry experience. Currently he is at Kraft Heinz, where he leads the research team in charge of demand forecasting. He has pioneered the development of best-in-class forecasting systems utilized by leading Fortune 500 companies. Jeff's approach combines a robust data-driven methodology with innovative strategies, enhancing forecasting models and business outcomes significantly. Leading cross-functional teams, Jeff has designed and implemented demand forecasting systems that have markedly improved forecast accuracy, inventory optimization, and customer satisfaction. His proficiency in statistical modeling, machine learning, and advanced analytics has led to the implementation of forecasting methodologies that consistently surpass industry norms. Jeff's strategic foresight and his capability to align forecasting initiatives with overarching business objectives have established him as a trusted advisor to senior executives and a prominent expert in the data science domain. Additionally, Jeff actively contributes to the open-source community, notably to PyTimeTK, where he develops tools that enhance time series analysis capabilities. He currently resides in Chicago, IL with his wife and son.

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