Modern Time Series Forecasting with Python. Explore industry-ready time series forecasting using modern machine learning and deep learning

(ebook) (audiobook) (audiobook)

Autor:: Manu Joseph

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Wydawnictwo:: Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
Ocena:: Bądź pierwszym, który oceni tę książkę
Stron:: 552
Dostępne formaty:: PDF

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Do przechowalni

We live in a serendipitous era where the explosion in the quantum of data collected and a renewed interest in data-driven techniques such as machine learning (ML), has changed the landscape of analytics, and with it, time series forecasting. This book, filled with industry-tested tips and tricks, takes you beyond commonly used classical statistical methods such as ARIMA and introduces to you the latest techniques from the world of ML.

This is a comprehensive guide to analyzing, visualizing, and creating state-of-the-art forecasting systems, complete with common topics such as ML and deep learning (DL) as well as rarely touched-upon topics such as global forecasting models, cross-validation strategies, and forecast metrics. You’ll begin by exploring the basics of data handling, data visualization, and classical statistical methods before moving on to ML and DL models for time series forecasting. This book takes you on a hands-on journey in which you’ll develop state-of-the-art ML (linear regression to gradient-boosted trees) and DL (feed-forward neural networks, LSTMs, and transformers) models on a real-world dataset along with exploring practical topics such as interpretability.

By the end of this book, you’ll be able to build world-class time series forecasting systems and tackle problems in the real world.

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O autorze 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

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Szczegóły książki

Tytuł oryginału:: Modern Time Series Forecasting with Python. Explore industry-ready time series forecasting using modern machine learning and deep learning
ISBN Ebooka:: 978-18-032-3204-1, 9781803232041
Data wydania ebooka :: 2022-11-24 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:: 25.5MB
Rozmiar pliku ePub:: 22.3MB

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Kategorie:
Big Data » Analiza danych

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. Multi-Step Forecasting
18. Evaluating Forecasts – Forecast Metrics
19. Evaluating Forecasts – Validation Strategies

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