Probabilistic Programming for Modeling Short Count Time Series
Sandeep Panchamukhi · Työväentutkimus Vuosikirja · 2019
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 acontext-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 specificupdaterules. 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.