Abstract: Quantitative forecasting methods can be divided into time series models and causal models.Causal models forecast by considering the effects of outside factors,while time series models attempt to predict the future values using historical data itself.However,time series models take into account the structure of historical data rather than the effects of causal factors,and causal models consider the effect of causal factors rather than the structure of history data Therefore,a forecasting method based on hidden Markov mo del(HMM) with multivariable data,which includes both the time series structure and causal factors,is proposed in this paper.Firstly,we introduce the basic theory of HMM;then the corresponding algorithm is developed after discussing model training and parameters estimation.At last,a simulation experiment and an empirical research are launched,and experimental results indicate that the model proposed is effective.

Key words: hidden markov model, expectation maximization algorithm, viterbi algorithm, causal factors, forecast