Transcription of Time Series Analysis in Python with statsmodels
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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), A
Granger causality f-test ===== Test statistic Critical Value p-value df-----1.248787 2.387325 0.289 (4, 579) ===== H_0: [’cpi’, ’realgdp’] do not Granger-cause m1 Conclusion: fail to reject H_0 at 5.00% significance level McKinney, Perktold, Seabold (statsmodels) Python Time Series Analysis SciPy Conference 2011 22 / 29 ...
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