Time Series Analysis in Python with statsmodels

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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) modelsFiltering tools (Hodrick-Prescott and others)Near future: Bayesian dynamic linear models (DLMs), ARCH /GARCH volatility models and beyondMcKinney, Perktol

Inferring structure, forecasting and simulation, and testing distributional assumptions about the data Modeling dynamic relationships among multiple time series Broad applications e.g. in economics, nance, neuroscience, signal processing... McKinney, Perktold, Seabold (statsmodels) Python Time Series Analysis SciPy Conference 2011 3 / 29

  Series, Python, Time, Modeling, Simulation, Time series, And simulation, In python, Python time series