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将可以很快上手并投入使用。
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
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