A State Space Modeling Approach for Time Series Forecasting
提出一种随机滤波方法，用于在线递归估计和预测自相关时间序列，介绍了非季节性和季节性时间序列的状态空间模型，并利用卡尔曼滤波器进行在线预测。
A stochastic filtering method is presented for on-line recursive estimation and forecasting of autocorrelated time series. Several state space models for nonseasonal and seasonal time series, which belong to the autoregressive integrated-moving average class, are presented. The Kalman filter is introduced as the recursive data processor for on-line time series forecasting. The estimation problem and initial values determination are discussed, and numerical examples are given. An extension of Brown's adaptive smoothing method for autocorrelated time series through the proposed filtering approach is also presented.