Spatio-temporal convolutional residual network for regional commercial vitality prediction

Abstract

The vitality of commercial entities reflects the business condition of their surrounding area, the prediction of which helps identify the trend of regional development and make investment decisions. The indicators of business conditions, like revenues and profits, can be employed to make a prediction beyond any doubt. Unfortunately, such figures constitute business secrets and are usually publicly unavailable. Thanks to the rapid growing of location based social networks such as Yelp and Foursquare, massive amount of online data has become available for predicting the vitality of commercial entities. In this paper, a Spatio-Temporal Convolutional Residual Neural Network (STCRNN) is proposed for regional commercial vitality prediction, based on public online data, such as reviews and check-ins from mobile apps. Firstly, a commercial vitality map is built to indicate the popularity of business entities. Afterwards, a local convolutional neural network is employed to capture the spatial relationship of surrounding commercial districts on the vitality map. Then, a 3-dimension convolution is applied to deal with both recent and periodic variations, i.e., the sequential and seasonal changes of commercial vitality. Finally, long short-term memory is introduced to synthesize these two variations. In particular, a residual network is used to eliminate gradient vanishing and exploding, caused by the increase of depth of neural networks. Experiments on public Yelp datasets from 2013 to 2018 demonstrate that STCRNN outperforms the current methods in terms of mean square error.