Before revision:  and Gaussian processes regression (GP approach) as two benchmarks, our approach presents itself in a more robust and flexible restricted form compared with the AR approach, while as a non-parametric approach, it achieves similar accuracy to the GP approach with much lower computational cost especially when the sample size is large . Popular techniques in time series analysis such as AR, ARCH and GARCH can be incorporated to our model .
After revision:  and Gaussian processes regression (GP approach) as two benchmarks, our approach presents itself in a more robust and flexible restricted form compared with the AR approach, while as a non-parametric approach, it achieves similar accuracy to the Gaussian Process (GP) approach with much lower computational cost especially when the sample size is large . Popular techniques in time series analysis such as AR, ARCH and GARCH can be incorporated to our model .