Parallelization of time series forecasting model

Abstract

We show a parallel neural network (cross-over prediction model) for time series statistical learning implemented in PVM ("parallel virtual machine") and MPI ("message passing interface"), in order to reduce computational time. Parallelization is achieved in two ways: updating autoregressive parameters via a genetic algorithm and evaluating the overall prediction function via a parallel neural network. PVM permits an heterogeneous collection of Unix computers networked together to be viewed by our program as a simple parallel computer. We show different architectures of parallel processors systems and discuss its computing model.