Transcription of Time Series Analysis in Python with statsmodels - SciPy
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Time Series Analysis in Python with statsmodels - SciPy
time Series Analysis in Python with statsmodelsWes McKinney1 Josef Perktold2 Skipper Seabold31 Department of Statistical ScienceDuke University2 Department of EconomicsUniversity of North Carolina at Chapel Hill3 Department of EconomicsAmerican University10thPython in Science Conference, 13 July 2011 McKinney, Perktold, Seabold ( statsmodels ) Python time Series AnalysisSciPy Conference 20111 / 29 What is statsmodels ?A library for statistical modeling, implementing standard statisticalmodels in Python using NumPy and SciPyIncludes:Linear (regression) models of many formsDescriptive statisticsStatistical testsTime Series much moreMcKinney, Perktold, Seabold ( statsmodels ) Python time Series AnalysisSciPy Conference 20112 / 29 What is time Series Analysis ?Statistical modeling of time -ordered data observationsInferring structure, forecasting and simulation, and testingdistributional assumptions about the dataModeling dynamic relationships among multiple time seriesBroad applications in economics, finance, neuroscience, , Perktold, Seabold ( statsmodels ) Python time Series AnalysisSciPy Conference 20113 / 29 Talk OverviewBrief update onstatsmodelsdevelopmentAside: user interface and data structuresDescriptive statistics and testsAuto-regressive moving average models (ARMA)Vector autoregres
Hodrick-Prescott (HP) lter separates a time series y t into a trend ˝ t and a cyclical component t, so that y t = ˝ t + t. 1962 1966 1970 1974 1978 1982 1986 1990 1994 1998 2002 2006 4 2 0 2 4 6 8 10 12 14 Inflation Cyclical component Trend component McKinney, Perktold, Seabold (statsmodels) Python Time Series Analysis SciPy Conference 2011 ...
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