December 2022
Intermediate to advanced
376 pages
11h 30m
Chinese
Overview
天气、股票、心跳都会产生时间序列数据，物联网、数字化医疗和智慧城市的兴起更是产生了大量的时间序列数据。随着数据的规模快速增长，应用机器学习和统计方法进行时间序列分析的做法越来越普遍，也越来越重要。
本书从实战角度介绍时间序列分析中的创新技术和实际用例，帮助你结合使用传统的统计方法和先进的机器学习技术来解决时间序列分析中的常见问题。由于Python和R都是时间序列分析常用的语言，因此本书兼顾这两种语言并对时间序列进行全面的阐释，可以让数据分析师、数据工程师和其他与数据打交道的读者快速上手。
- 发现并整理时间序列数据
- 针对时间序列进行探索性数据分析
- 模拟时间序列
- 存储时间序列
- 为时间序列生成并选择特征
- 测量误差
- 使用机器学习和深度学习分析时间序列
- 评估模型的精度和性能
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