Probabilistic programming for modeling short count time series

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http://urn.fi/URN:NBN:fi-fe201804208655
Title: Probabilistic programming for modeling short count time series
Author: Panchamukhi, Sandeep
Contributor: University of Helsinki, Faculty of Science, Department of Computer Science
Publisher: Helsingin yliopisto
Date: 2018
Language: eng
URI: http://urn.fi/URN:NBN:fi-fe201804208655
http://hdl.handle.net/10138/273577
Thesis level: master's thesis
Abstract: Time series analysis has been a popular research topic in the last few decades. In this thesis, we develop time series models to investigate short time series of count data. We first begin with Poisson autoregressive model and extend it to capture day effects explicitly. Then we propose hierarchical Poisson tensor factorization model as an alternative to the traditional count time series models. Furthermore, we suggest a context-based model as an improvement over hierarchical Poisson tensor factorization model. We implement the models in an open-source probabilistic programming framework Edward. This tool enables us to express the models in form of executable program code and allows us to rapidly prototype models without the need of derivation of model specific update rules. We also explore strategies for selecting the best model out of alternatives. We study the proposed models on a dataset containing media consumption data. Our experimental findings demonstrate that the hierarchical Poisson tensor factorization model significantly outperforms the Poisson autoregressive models in predicting event counts. We also visualize the key results of our exploratory data analysis.


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