Spatio-Temporal Symbolization of Multidimensional Time Series

The present study proposes a new symbolization algorithm for multidimensional time series. We view temporal sequences as observed data generated by a dynamical system, and therefore the goal of symbolization is to estimate symbolic sequences that minimize loss of information, which is called generating partition in nonlinear physics. In order to utilize the theoretical property of symbol dynamics in data mining, our algorithm estimates symbols on multivariate time series by integrating both spatial and temporal information and selecting those dimensions in multidimensional time series containing useful information. Probabilistic symbolic sequences derived from our symbolization method can be used in various supervised and unsupervised data-mining tasks. To demonstrate this, the algorithm is evaluated by applying it to both simulated data and a real-world dataset. In both cases, the new algorithm outperforms its alternative approaches.