Vector ARMA Parameter Estimation Method And Its Application

Vector autoregressive moving-average (VARMA) model has been found a widerange of applications in social economics, industrial production modeling,biomedical signal processing, speech signal processing and time series analysis.Many methods for scalar ARMA parameter estimation were developed. However,these methods cannot be easily extended to vector ARMA parameter estimation dueto vector model complexity. The first part of this article we proposes a fastalgorithm for vector autoregressive moving-average (ARMA) parameter estimationunder noise environments. By using an equivalent AR parameter model techniqueand a Yule-Walker equation technique, we transform the parameter estimationproblem of the VARMA model into linear equations. AS a result, the proposedalgorithm has a lower computational complexity and thus has a faster speed than theconventional algorithms. Illustrative examples show that the proposed algorithm hasgood performance in computation. The second part of this article the algorithm isapplied to time series analysis and forecasting. Comparison with other methods theARMA model takes into account the situation of noise, so it have wider practicalapplication, more over the methods based on ARMA model have more accurateprediction.