Early Classification of Ongoing Observation

This work focuses on early classification of ongoing observation of the object, which is beneficial for a number of applications that require time-critical decision making. We propose an approach for discovering two key aspects of multivariate time series (m.t.s.) observation, (1) Temporal Dynamics and (2) Sequential Cues. The key idea is that m.t.s. Observation can be represented as an instantiation of a Multivariate Marked Point-Process (Multi-MPP). Each variable characterizes the temporal dynamics of a particular feature event of an object, where both timing and strength information of that feature event are preserved. To make this model computationally practical, we introduce the Multilevel-Discretized Marked Point-Process (MD-MPP) model which can ensure a good piece-wise stationary property both in the time-domain and mark-space while preserving dynamics as much as possible. Based on this model, another important temporal patterns of early classification, sequential cues among variables, becomes formalizable. We construct a probabilistic suffix tree to represent sequential patterns among features in terms of Variable order Markov Model (VMM). The effectiveness of our approach is evaluated on three experimental scenarios. Our method achieves superior performance for early classification of ongoing m.t.s. Observation data.