Residual Convolutional LSTM for Tweet Count Prediction

Abstract

The tweet count prediction of a local spatial region is to forecast the number of tweets that are likely to be posted from that area over a relatively short period of time. It has many applications such as human mobility analysis, traffic planning, and abnormal event detection. In this paper, we formulate tweet count prediction as a spatiotemporal sequence forecasting problem and design an end-to-end convolutional LSTM based network with skip connection for this problem. Such a model enables us to exploit the unique properties of spatiotemporal data, consisting of not only the temporal characteristics such as temporal closeness, period and trend properties but also spatial dependencies. Our experiments on the city of Seattle, WA as well as a larger city of New York City show that the proposed method consistently outperforms the competitive baseline approaches.