A self-supervised learning approach for denoising autoregressive models with additive noise: finite and infinite variance cases
加性噪声自回归模型的自监督去噪：有限与无限方差情形
The autoregressive time series model is a popular second-order stationary process, modeling a wide range of real phenomena. However, in applications, autoregressive signals are often corrupted by additive noise. Further, the autoregressive process and the corruptive noise may be highly impulsive, stemming from an infinite-variance distribution. The model estimation techniques that account for additional noise tend to show reduced efficacy when there is very strong noise…