Jeffrey Yau: Applied Time Series Econometrics in Python and R | PyData San Francisco 2016

Jeffrey Yau: Applied Time Series Econometrics in Python and R
PyData San Francisco 2016

Time series data is ubitious, and time series statistical models should be included in any data scientists’ toolkit. This tutorial covers the mathematical formulation, statistical foundation, and practical considerations of one of the most important classes of time series models: the AutoRegression Integrated Moving Average with Explanatory Variables model and its seasonal counterpart.

Time series data is ubitious, both within and out of the field of data science: weekly initial unemployment claim, tick level stock prices, weekly company sales, the daily number of steps taken recorded by a wearable, just to name a few. Some of the most important and commonly used data science techniques to analyze time series data are those in developed in the field of statistics. For this reason, time series statistical models should be included in any data scientists’ toolkit.

This 120-minute tutorial covers the mathematical formulation, statistical foundation, and practical considerations of one of the most important classes of time series models, AutoRegression Integrated Moving Average with Explanatory Variables (ARIMAX) models, and its Seasonal counterpart (SARIMAX).

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