实用时间序列分析(影印版)(英文版)

随着物联网、数字医 疗、智慧城市的兴起，时 间序列数据分析变得越来 越重要。随着持续监测和 数据收集变得越来越普遍 ，对通过统计和机器学习 技术进行时间序列分析的 需求将会增长。 这本实用指南涵盖了 时间序列数据分析的创新 成果和现实世界的案例， 使用传统统计方法和现代 机器学习技术，帮你应对 时间序列中最常见的数据 工程和分析挑战。作者 Aileen Nielsen用R和 Python语言对时间序列进 行了全面且通俗易懂的介 绍，数据科学家、软件工 程师和研究人员都可以很 快上手并投入使用。 通过本书你将学会： ·查询并清洗时间序列 数据 ·进行探索性时间序列 数据分析 ·存储临时数据 ·模拟时间序列数据 ·为时间序列生成和选 择特性 ·测量误差 ·利用机器学习或深度 学习对时间序列进行预测 和分类 ·评估模型准确性和性 能

Preface 1．TimeSeries：AnOverviewand aQuickHistory The History of Time Series in Diverse Applications Medicine as a Time Series Problem Forecasting Weather Forecasting Economic Growth Astronomy Time Series Analysis Takes Off The Origins of Statistical Time Series Analysis The Origins of Machine Learning Time Series Analysis More Resources 2．FindingandWranglingTimeSeriesData where to Find Time Series Data Prepared Data Sets Found Time Series Retrofitting a Time Series Data Collection from a Collection of Tables A Worked Example：Assembling a Time Series Data Collection Constructing a Found Time Series Timestamping Troubles Whose Timestamp Guesstimating Timestamps to Make Sense of Data What’s a Meaningful Time Scale Cleaning Your Data Handling Missing Data Upsampling and Downsampling Smoothing Data Seasonal Data Time Zones Preventing Lookahead More Resources 3．ExploratoryDataAnalysisforTimeSeries Familiar Methods Plotting Histograms Scatter Plots Time Series-Specific Exploratory Methods Understanding Stationarity Applying Window Functions Understanding and Identifying Self-Correlation Spurious Correlations Some Useful Visualizations lD Visualizations 2D Visualizations 3D Visualizations More Resources 4．SimulatingTimeSeriesData What’S Special About Simulating Time Series Simulation Versus Forecasting Simulations in Code Doing the Work Yourself