Discovering and Characterizing Hidden Variables in Streaming Multivariate Time Series

@article{Ray2010DiscoveringAC,
  title={Discovering and Characterizing Hidden Variables in Streaming Multivariate Time Series},
  author={Soumi Ray and Tim Oates},
  journal={2010 Ninth International Conference on Machine Learning and Applications},
  year={2010},
  pages={913-916}
}
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… CONTINUE READING