Modern Time Series Forecasting with Python. Industry-ready machine learning and deep learning time series analysis with PyTorch and pandas - Second Edition

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Autorzy:: Manu Joseph, Jeffrey Tackes, Christoph Bergmeir

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Modern Time Series Forecasting with Python. Industry-ready machine learning and deep learning time series analysis with PyTorch and pandas - Second Edition Manu Joseph, Jeffrey Tackes, Christoph Bergmeir - okladka książki

Serie wydawnicze:: Learning
Wydawnictwo:: Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
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Modern Time Series Forecasting with Python. Explore industry-ready time series forecasting using modern machine learning and deep learning Manu Joseph

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Predicting the future, whether it's market trends, energy demand, or website traffic, has never been more crucial. This practical, hands-on guide empowers you to build and deploy powerful time series forecasting models. Whether you’re working with traditional statistical methods or cutting-edge deep learning architectures, this book provides structured learning and best practices for both.
Starting with the basics, this data science book introduces fundamental time series concepts, such as ARIMA and exponential smoothing, before gradually progressing to advanced topics, such as machine learning for time series, deep neural networks, and transformers. As part of your fundamentals training, you’ll learn preprocessing, feature engineering, and model evaluation. As you progress, you’ll also explore global forecasting models, ensemble methods, and probabilistic forecasting techniques.
This new edition goes deeper into transformer architectures and probabilistic forecasting, including new content on the latest time series models, conformal prediction, and hierarchical forecasting. Whether you seek advanced deep learning insights or specialized architecture implementations, this edition provides practical strategies and new content to elevate your forecasting skills.

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O autorach książki

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

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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Szczegóły książki

Tytuł oryginału:: Modern Time Series Forecasting with Python. Industry-ready machine learning and deep learning time series analysis with PyTorch and pandas - Second Edition
ISBN Ebooka:: 978-18-358-8319-8, 9781835883198
Data wydania ebooka :: 2024-10-31 Data wydania ebooka często jest dniem wprowadzenia tytułu do sprzedaży i może nie być równoznaczna z datą wydania książki papierowej. Dodatkowe informacje możesz znaleźć w darmowym fragmencie. Jeśli masz wątpliwości skontaktuj się z nami sklep@helion.pl.
Język publikacji:: 1
Rozmiar pliku Pdf:: 44.8MB
Rozmiar pliku ePub:: 49.7MB

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Kategorie:
Programowanie » Python - Programowanie
Big Data
Serie wydawnicze: Learning

Spis treści książki

1. Introducing Time Series
2. Acquiring and Processing Time Series Data
3. Analyzing and Visualizing Time Series Data
4. Setting a Strong Baseline Forecast
5. Time Series Forecasting as Regression
6. Feature Engineering for Time Series Forecasting
7. Target Transformations for Time Series Forecasting
8. Forecasting Time Series with Machine Learning Models
9. Ensembling and Stacking
10. Global Forecasting Models
11. Introduction to Deep Learning
12. Building Blocks of Deep Learning for Time Series
13. Common Modeling Patterns for Time Series
14. Attention and Transformers for Time Series
15. Strategies for Global Deep Learning Forecasting Models
16. Specialized Deep Learning Architectures for Forecasting
17. Probabilistic Forecasting and More
18. Multi-Step Forecasting
19. Evaluating Forecast Errors—A Survey of Forecast Metrics
20. Evaluating Forecasts – Validation Strategies

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Modern Time Series Forecasting with Python. Industry-ready machine learning and deep learning time series analysis with PyTorch and pandas - Second Edition