Discovering and Characterizing Hidden Variables in Streaming Multivariate Time Series

Time series data naturally arises in many domains, such as industrial process control, robotics, finance, medicine, climatology, and numerous others. In many cases variables known to be causally relevant cannot be measured directly or the existence of such variables is unknown. This paper presents an extension of the neural network architecture, called the LO-net [1], for inferring both the existence and values of hidden variables in streaming multivariate time series, leading to deeper understanding of the domain and more accurate prediction. The core idea is to initially make predictions with one network (the observable or O net) based on a time delay embedding, following this with a gradual reduction in the temporal scope of the embedding that forces a second network (the latent or L net) to learn to approximate the value of a single hidden variable, which is then input to the O net based on the original time delay embedding. Experiments show that the architecture efficiently and accurately identifies the number of hidden variables and their values over time.