Felix Marczinowski, Philipp Mack, Sönke Niekamp- Exploratory Time Series Analysis of NYC Subway Data
752 views · Published 26 July 2014 · 1:18:41 · Indexed 4 November 2026
Channel: PyData · 2014 · Science & Technology
PyData 2014 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. 00:00 Welcome! 00:10 Help us add time stamps or captions to this video! See the description for details. Want to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps
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