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Exploratory Time Series Analysis of NYC Subway Data

Description

What questions arise during a quick model assessment? In this hands-on-tutorial we want to cover the whole chain from preparing data to choosing and fitting a model to properly assessing the quality of a predictive model. Our dataset in this tutorial are the numbers of people entering and exiting New York subway stations. Among other ways of building a predictive model, we introduce the python package pydse ( http://pydse.readthedocs.org/ ) and apply it to the dataset in order to derive the parameters of an ARMA-model (autoregressive moving average). At the end of the tutorial we evaluate the models and examine the strengths and weaknesses of various ways to measure the accuracy and quality of a predictive model.

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