Deep learning on symbolic representations for large-scale heterogeneous time-series event prediction

Shengdong Zhang, Soheil Bahrampour, Naveen Ramakrishnan, Lukas Schott, Mohak Shah

Abstract: In this paper, we consider the problem of event prediction with multi-variate time series data consisting of heterogeneous (continuous and categorical) variables. The complex dependencies between the variables combined with asynchronicity and sparsity of the data makes the event prediction problem particularly challenging. Most state-of-art approaches address this either by designing hand-engineered features or breaking up the problem over homogeneous variates. In this work, we formulate the (rare) event prediction task as a classification problem with a novel asymmetric loss function and propose an end-to-end deep learning algorithm over symbolic representations of time-series. Symbolic representations are fed into an embedding layer and a Long Short Term Memory Neural Network (LSTM) layer which are trained to learn discriminative features. We also propose a simple sequence chopping technique to speed-up the training of LSTM for long temporal sequences. Experiments on real-world industrial datasets demonstrate the effectiveness of the proposed approach.