%AQian, Cheng%AKargas, Nikos%AXiao, Cao%AGlass, Lucas%ASidiropoulos, Nicholas%ASun, Jimeng%Anull Ed.%D2021%I %K %MOSTI ID: 10298957 %PMedium: X %TMulti-version Tensor Completion for Time-delayed Spatio-temporal Data %X

Real-world spatio-temporal data is often incomplete or inaccurate due to various data loading delays. For example, a location-disease-time tensor of case counts can have multiple delayed updates of recent temporal slices for some locations or diseases. Recovering such missing or noisy (under-reported) elements of the input tensor can be viewed as a generalized tensor completion problem. Existing tensor completion methods usually assume that i) missing elements are randomly distributed and ii) noise for each tensor element is i.i.d. zero-mean. Both assumptions can be violated for spatio-temporal tensor data. We often observe multiple versions of the input tensor with different under-reporting noise levels. The amount of noise can be time- or location-dependent as more updates are progressively introduced to the tensor. We model such dynamic data as a multi-version tensor with an extra tensor mode capturing the data updates. We propose a low-rank tensor model to predict the updates over time. We demonstrate that our method can accurately predict the ground-truth values of many real-world tensors. We obtain up to 27.2% lower root mean-squared-error compared to the best baseline method. Finally, we extend our method to track the tensor data over time, leading to significant computational savings.

Country unknown/Code not availablehttps://doi.org/10.24963/ijcai.2021/400OSTI-MSA