Repeated Time Series Analysis of ARIMA-Noise Models
发展了重复时间序列测量的理论与方法，放松了ARIMAN模型的正态性假设，并讨论了各分量序列的模型识别、估计和预测。
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.