| 简介： | We propose an Adaptive Functional Autoregressive (AFAR) forecast model to predict electricity price curves. With time-varying operators, the AFAR model can be safely used in both stationary and non-stationary situations. A closed-form maximum likelihood (ML) estimator is derived under stationarity. The result is further extended for non-stationarity, where the time-dependent operators are adaptively estimated under local homogeneity. We provide the theoretical results of the ML estimator and the adaptive estimator. Simulation study illustrates nice finite sample performance of the AFAR modeling. The AFAR model also exhibits a superior accuracy in the forecast exercise of the California electricity daily price curves compared to several alternatives. |