Exhibition Program

Analysis of spatio-temporal data is a common research topic that requires the interpolations of unobserved locations and the predictions of feature observations by utilizing information about where and when the data were observed. One of the most difficult problems is to make future predictions of unobserved locations. Tensor factorization methods are popular in this field because of their capability of handling multiple types of spatiotemporal data, dealing with missing values, and providing computationally efficient parameter estimation procedures. We propose a new tensor factorization method that estimates low-rank latent factors by simultaneously learning the spatial and temporal correlations. We introduce new spatial autoregressive regularizers based on existing spatial autoregressive models and provide an efficient estimation procedure.