Repeated Time Series Analysis of ARIMA–Noise Models
为重复测量的ARIMA-噪声过程开发了理论和方法，允许放松正态性假设并识别各分量序列的模型，讨论了估计和预测，适合时间序列研究者。
This article develops a theory and methodology for repeated time series (RTS) measurements on autoregressive integrated moving average–noise (ARIMAN) process. The theory enables us to relax the normality assumption in the ARIMAN model and to identify models for each component series of the process. We discuss the properties, estimation, and forecasting of RTS ARIMAN models and illustrate with examples.