Revisions in ARIMA Signal Extraction
研究了从ARIMA过程中提取信号时，初步估计会随新数据而修正的问题，发现修正序列服从平稳ARMA过程，且所有可分解的修正仅相差一个尺度因子，其中规范分解使修正方差最大。
Abstract The problem of decomposing an observed series, assumed to follow an ARIMA process, into signal plus noise is considered. It is well known that the preliminary estimates of the signal will be subject to revisions as more data become available. For a general ARIMA process, the revision in the concurrent estimate of the signal is seen to follow a stationary ARMA process, easily derived from the overall series model. The results are extended to non-concurrent preliminary estimates. Finally, it is found that, except for a scale factor, the revisions are the same for all admissible decompositions and the canonical decomposition maximizes the variance of the revision.