CoupledMUTS: Coupled Multivariate Utility Time-Series Representation and Prediction
Siyuan Ren, Bin Guo, Ke Li, Qianru Wang, Zhiwen Yu, Longbing Cao
Abstract
Ubiquitous Internet of Things (IoT) sensors in the smart city generate various urban utility sequential data, such as electricity and water usage records, which are defined as multivariate utility time series (MUTS). Due to the complex behavior of human beings, MUTS contains more complicated relationships, which go beyond general multivariate time series (TS). Specifically, multifaceted temporal couplings exist in MUTS, including intra-/inter-TS, short-to-long term, evolving, and polarized (positive/negative) relationships. Existing multisequence predictors including the latest deep-learning methods either weaken short-to-long term representation or omit evolving and polarized couplings. This work focuses on MUTS sensory data representation and prediction and proposes a novel approach—CoupledMUTS for multifaceted temporal coupling relational learning. MUTS representation module generates multidimensional representations to reveal short-to-long temporal couplings in MUTS. Gated coupling units (GCUs) module learns evolving couplings by filtering weak positive/negative relations. And dual-stage fusion module integrates multifaceted temporal couplings in both intra-TS and inter-TS for prediction. Extensive experiments on two real-world utility data sets demonstrate that our method outperforms existing shallow and deep models in utility demand prediction.