Forecasting user behaviors in call detail records using LSTM models

Kocaman, Hasan
City planners, governors and mobile phone operators utilize quite from Call DetailRecords (CDR) data in the fields like optimizations of traffic congestion, event de-tection, billing and advertisement policies. Both individual and crowd analyses helpimproving the quality of services.In this thesis, we present regression and classification analysis for three main prob-lems. In regression analysis, we experiment on call counts and call time-sums ofspecified numbers of users. We propose cluster based and outlier separating modelsin these two tasks for the purpose of improving the results of individual user-basedmodels comprised of various Long Short Term Memory(LSTM) layers. In the clas-sification analysis, on the other hand, we present models that predict next locationson the trajectories of the users. We improve the results of base LSTM model withtwo-predictions-at-once approach. The analyses show that recurrent neural networkswork well with sequential data and optimizations on top of the models yield promis-ing results.
Citation Formats
H. Kocaman, “Forecasting user behaviors in call detail records using LSTM models,” M.S. - Master of Science, Middle East Technical University, 2021.