Recurrent neural networks serve as black-box models for nonlinear dynamical systems identification and time series prediction. Training of recurrent networks typically minimizes t...
This paper describes an evolutionary method for identifying a causal model from the observed time series data. We use a system of ordinary differential equations (ODEs) as the cau...
Computational finance leverages computer technologies to build models from large amounts of data to extract insight. In today's networked world, the amount of data available t...
Badrish Chandramouli, Mohamed H. Ali, Jonathan Gol...
Database support for multidimensional arrays is an area of growing importance; a variety of highvolume applications such as spatio-temporal data management and statistics/OLAP bec...
Peter Baumann, Andreas Dehmel, Paula Furtado, Rola...
We present a hybrid method to turn off-the-shelf information retrieval (IR) systems into future event predictors. Given a query, a time series model is trained on the publication...