Time Series Analysis in Python with statsmodels - SciPy

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time Series Analysis in Python with statsmodelsWes McKinney1Josef Perktold2Skipper Seabold31Department of Statistical ScienceDuke University2Department of EconomicsUniversity of North Carolina at Chapel Hill3Department of EconomicsAmerican University10thPython in Science Conference, 13 July 2011McKinney, Perktold, Seabold ( statsmodels ) Python time Series AnalysisSciPy Conference 20111 / 29What 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 / 29What 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 / 29Talk OverviewBrief update onstatsmodelsdevelopmentAside: user interface and data structuresDescriptive statistics and testsAuto-regressive moving average models (ARMA)Vector autoregression (VAR)

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 ...

  Series, Python, Time, Time series, Python time series