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ARIMA模型 Python.pdf

发布时间：2022-05-31 发布人：admin 分类：说明书 资料大小：6.27M 资料格式：pdf 举报 版权申诉
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    TIME SERIES ANALYSIS WITH PYTHON Aileen Nielsen July, 13, 2016 aileen.a.nielsen@gmail.com
    INSTALLATION INSTRUCTIONS • Please install Conda per ‘quick install’ instructions: http://conda.pydata.org/docs/install/quick.html • Make sure you have the following packages installed: • • • • • pandas numpy Statsmodels scikit-learn scipy • These would be good to have but are not essential: • • pytz hmmlearn
    OUTLINE • Why time series? • Quick Pandas intro • Dealing with dates in Pandas • Reading + manipulating time-stamped data • Common time series analytical tools • Prediction • Classification
    CAVEATS • Time series analysis is a particularly tricky & controversial field • I’ll give some background as we move ahead, but you need to read more when you want to do a real analysis • Tests for goodness of fit, etc, are particularly error prone in time series analysis • Whenever I don’t specify, but should, assume it’s iid normally distributed (‘error’ terms)
    WHAT’S SPECIAL ABOUT TIME SERIES?
    WHERE DO TIME SERIES POP UP? • Many of the most controversial questions arise from time series analysis • Whenever we want to know the future, we’re pretty much stuck with time series analysis • Ditto for thinking about causality in ‘natural experiments’ http://www.amstat.org/publications/jse/v21n1/witt.pdf http://www.forbes.com/sites/neilhowe/2015/05/28/whats-behind-the-decline-in- crime/#3e1e6be07733
    SPEECH RECOGNITION http://www.amstat.org/publications/jse/v21n1/witt.pdf http://www.forbes.com/sites/neilhowe/2015/05/28/whats-behind-the-decline-in- crime/#3e1e6be07733
    PHYSICS EXPERIMENTS
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