Modern time series forecasting with Python: industry-ready machine learning and deep learning time series analysis with PyTorch and pandas
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...
Gespeichert in:
| Beteiligte Personen: | , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
Birmingham, UK
Packt Publishing Ltd.
2024
|
| Ausgabe: | Second edition. |
| Schriftenreihe: | Expert insight
|
| Schlagwörter: | |
| Links: | https://learning.oreilly.com/library/view/-/9781835883181/?ar |
| Zusammenfassung: | 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. |
| Beschreibung: | Includes bibliographical references and index |
| Umfang: | 1 Online-Ressource (658 Seiten) illustrations |
| ISBN: | 1835883184 9781835883181 9781835883198 1835883192 |
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| id | ZDB-30-ORH-110159772 |
| illustrated | Illustrated |
| indexdate | 2025-01-17T11:22:06Z |
| institution | BVB |
| isbn | 1835883184 9781835883181 9781835883198 1835883192 |
| language | English |
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| physical | 1 Online-Ressource (658 Seiten) illustrations |
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| publishDate | 2024 |
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| publisher | Packt Publishing Ltd. |
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| spelling | Joseph, Manu VerfasserIn aut Modern time series forecasting with Python industry-ready machine learning and deep learning time series analysis with PyTorch and pandas Manu Joseph, Jeffrey Tackes Second edition. Birmingham, UK Packt Publishing Ltd. 2024 1 Online-Ressource (658 Seiten) illustrations Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Expert insight Includes bibliographical references and index 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. Time-series analysis Data processing Forecasting Data processing Python (Computer program language) Machine learning Série chronologique ; Informatique Prévision ; Informatique Python (Langage de programmation) Apprentissage automatique Tackes, Jeffrey VerfasserIn aut |
| spellingShingle | Joseph, Manu Tackes, Jeffrey Modern time series forecasting with Python industry-ready machine learning and deep learning time series analysis with PyTorch and pandas Time-series analysis Data processing Forecasting Data processing Python (Computer program language) Machine learning Série chronologique ; Informatique Prévision ; Informatique Python (Langage de programmation) Apprentissage automatique |
| title | Modern time series forecasting with Python industry-ready machine learning and deep learning time series analysis with PyTorch and pandas |
| title_auth | Modern time series forecasting with Python industry-ready machine learning and deep learning time series analysis with PyTorch and pandas |
| title_exact_search | Modern time series forecasting with Python industry-ready machine learning and deep learning time series analysis with PyTorch and pandas |
| title_full | Modern time series forecasting with Python industry-ready machine learning and deep learning time series analysis with PyTorch and pandas Manu Joseph, Jeffrey Tackes |
| title_fullStr | Modern time series forecasting with Python industry-ready machine learning and deep learning time series analysis with PyTorch and pandas Manu Joseph, Jeffrey Tackes |
| title_full_unstemmed | Modern time series forecasting with Python industry-ready machine learning and deep learning time series analysis with PyTorch and pandas Manu Joseph, Jeffrey Tackes |
| title_short | Modern time series forecasting with Python |
| title_sort | modern time series forecasting with python industry ready machine learning and deep learning time series analysis with pytorch and pandas |
| title_sub | industry-ready machine learning and deep learning time series analysis with PyTorch and pandas |
| topic | Time-series analysis Data processing Forecasting Data processing Python (Computer program language) Machine learning Série chronologique ; Informatique Prévision ; Informatique Python (Langage de programmation) Apprentissage automatique |
| topic_facet | Time-series analysis Data processing Forecasting Data processing Python (Computer program language) Machine learning Série chronologique ; Informatique Prévision ; Informatique Python (Langage de programmation) Apprentissage automatique |
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