使用Python进行现代时间序列预测-第二版（早期版本）

English | 2024 | ISBN: 9781835883181 | 181 Pages | True EPUB | 6.39 MB

Learn traditional and cutting-edge Machine Learning (ML) and deep learning techniques and best practices for time series forecasting with Python, including global ML models, conformal prediction, and transformer architectures

Key Features Work through examples of how to use machine learning and global machine learning models for forecasting Enhance your time series toolkit by using deep learning models, including RNNs, transformers, and N-BEATS Learn probabilistic forecasting with conformal prediction and quantile regressions Purchase of the print or Kindle book includes a free eBook in PDF format Book Description 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. With Modern Time Series Forecasting with Python, Second Edition, you'll master cutting-edge deep learning architectures and advanced statistical techniques alongside classic methods like ARIMA and exponential smoothing. Learn the fundamentals from preprocessing, feature engineering, and evaluation to applying powerful machine and deep learning models, including ensemble and global methods.

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.

What you will learn Build machine learning models for regression-based time series forecasting Apply powerful feature engineering techniques to enhance prediction accuracy Tackle common challenges like non-stationarity and seasonality Combine multiple forecasts using ensembling and stacking for superior results Explore cutting-edge advancements in probabilistic forecasting and handle intermittent or sparse time series Evaluate and validate your forecasts using best practices and statistical metrics

Who this book is for This book is ideal for data scientists, quantitative analysts, financial analysts, meteorologists, risk analysts, and anyone interested in leveraging Python for accurate time series forecasting.

英文| 2024 |国际标准图书编号：9781835883181 | 181页|真EPUB | 6.39 MB 学习传统和尖端的机器学习（ML）和深度学习技术以及使用Python进行时间序列预测的最佳实践，包括全局ML模型、保形预测和变换器架构 关键功能学习如何使用机器学习和全局机器学习模型进行预测的示例通过使用深度学习模型（包括RNN、Transformer和N-BEATS）来增强你的时间序列工具包学习保形预测和分位数回归的概率预测购买印刷品或Kindle书籍包括一本PDF格式的免费电子书书籍描述预测未来，无论是市场趋势、能源需求还是网站流量，从未如此重要。这本实用的实践指南使您能够构建和部署强大的时间序列预测模型。借助Python现代时间序列预测第二版，您将掌握尖端的深度学习架构和先进的统计技术，以及ARIMA和指数平滑等经典方法。学习从预处理、特征工程和评估到应用强大的机器和深度学习模型（包括集成和全局方法）的基础知识。 这个新版本深入探讨了变压器架构和概率预测，包括最新时间序列模型、共形预测和分层预测的新内容。无论您是寻求先进的深度学习见解还是专门的架构实现，本版都提供了实用的策略和新的内容来提高您的预测技能。 您将学习的内容为基于回归的时间序列预测构建机器学习模型应用强大的特征工程技术来提高预测精度应对非平稳性和季节性等常见挑战使用集合和堆叠组合多个预测以获得卓越的结果探索概率预测的最新进展并处理间歇或稀疏的时间序列使用最佳实践和统计指标评估和验证您的预测 这本书非常适合数据科学家、定量分析师、金融分析师、气象学家、风险分析师以及任何有兴趣利用Python进行准确时间序列预测的人。

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