Research on condition trend prediction based on weighed hidden markov and autoregressive model

A novel trend prediction approach based on weighed hidden Markov model (HMM) and autoregressive model (AR) is presented in order to solve this problem of trend prediction for complex electronic system. This approach regards the autoregressive model as the output of HMM, uses weighted prediction method and mixed Gaussianin model to predict the hidden state of Markov chain, and calculates the output of model by using the regression coefficient of the maximum probability hidden state. This approach is applied to the trend prediction of complex chaotic time series and typical electronic equipment's BIT data, and the effects of various model parameters on trend prediction precision are discussed. The experiments based on condition trend prediction for electronic equipments demonstrate the effectiveness of the method.