 | | 系統號碼 | 965518 | | 書刊名 | Modern time series forecasting with Python : industry-ready machine learning and deep learning time series analysis with Pytorch and pandas / | | 主要著者 | Joseph, Manu, author. | | 其他著者 | Tackes, Jeffrey,;Bergmeir, Christoph, | | 出版項 | Birmingham, United Kingdom : packt, 2024.;Birmingham, United Kingdom : packt, ©2024 | | 索書號 | QA280.J67 2024 | | ISBN | 1835883184 | | 標題 | Python (Computer program language)
Time-series analysis.
Machine learning.
Python (Langage de programmation)
Apprentissage automatique.
Se?rie chronologique. | | | |
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| | 資料類型 | 狀態 | 應還日期 | 預約人數 | 館藏地 | 索書號 | 條碼號 | | 找書 | 圖書 | 在架上 | | 0 | 總館
西文圖書區  | QA280 .J67 2024 | W115865 |
| 內容簡介 | "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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