隨著物聯(lián)網(wǎng)、數(shù)字醫(yī)療、智慧城市的興起,時間序列數(shù)據(jù)分析變得越來越重要。隨著持續(xù)監(jiān)測和數(shù)據(jù)收集變得越來越普遍,對通過統(tǒng)計和機(jī)器學(xué)習(xí)技術(shù)進(jìn)行時間序列分析的需求將會增長。這本實用指南涵蓋了時間序列數(shù)據(jù)分析的創(chuàng)新成果和現(xiàn)實世界的案例,使用傳統(tǒng)統(tǒng)計方法和現(xiàn)代機(jī)器學(xué)習(xí)技術(shù),幫你應(yīng)對時間序列中最常見的數(shù)據(jù)工程和分析挑戰(zhàn)。作者Aileen Nielsen用R和Python語言對時間序列進(jìn)行了全面且通俗易懂的介紹,數(shù)據(jù)科學(xué)家、軟件工程師和研究人員將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
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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
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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