Three-stage multi-innovation parameter estimation for an exponential autoregressive time-series model with moving average noise by using the data filtering technique

This paper studies the data filtering-based identification algorithms for an exponential autoregressive time-series model with moving average noise. By means of the data filtering technique and the hierarchical identification principle, the identification model is transformed into three sub-identification (Sub-ID) models, and a filtering-based three-stage extended stochastic gradient algorithm is derived for identifying these Sub-ID models. In order to improve the parameter estimation accuracy, a filtering-based three-stage multi-innovation extended stochastic gradient (F-3S-MIESG) algorithm is developed by using the multi-innovation identification theory. The simulation results indicate that the proposed F-3S-MIESG algorithm can work well.

Automated summary

This paper explores data filtering-based identification algorithms for an exponential autoregressive time-series model with moving average noise. By using the hierarchical identification principle, the model is transformed into three sub-identification models and a new extended stochastic gradient algorithm is derived. Through simulation results, it is shown that the proposed algorithm can effectively improve parameter estimation accuracy.