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Time Series Analysis in Python with statsmodels

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

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

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