Tensor Time Series

Tensor time series analysis focuses on modeling and analyzing data with multiple dimensions and temporal dependencies, going beyond traditional univariate or multivariate time series. Current research emphasizes developing efficient algorithms, such as those based on low-rank tensor decompositions and Gaussian mixture models, to handle the high dimensionality and complex structure inherent in this data type, often employing contextual bandit frameworks for decision-making tasks. This field is significant for its potential to improve forecasting accuracy in diverse applications like targeted advertising, transportation demand prediction, and environmental monitoring, where multi-source spatio-temporal data is prevalent.