Parallel and Distributed Computational Multivariate Time Series Modeling in the State Space

In this paper a parallel and distributed computational procedure using a subspace method developed by Masanao Aoki for state space modeling of multivariate time series is proposed and implemented. The parallel solution of the Riccati equation due to the large computational effort it requires receives a special attention. For model evaluation, short time predictions, where a central role is played by a Kalman filtering approach are tested and some results are presented.