Neural networks for nonlinear regression with serially correlated disturbances: Evidence from cloud cover
带序列相关扰动的非线性回归神经网络:来自云量的证据
We propose a new treatment of nonlinear regression with serially correlated disturbances that incorporates autoregressive moving average structures into feedforward neural networks. The resulting model provides an alternative to modeling temporal dependence using lagged variables. In simulations, the proposed method accurately recovers regression functions of varying complexity and the underlying error dynamics across a range of time-series lengths and signal-to-noise…