Title: Modeling Seasonality and Serial Dependence of Electricity Price Curves with Warping Functional Autoregressive Dynamics

Speaker: Jiejie Zhang, Research Assistant, National University of Singapore

Time: 2018.12月22日 （4个课时）

Location: Center for Mathematical Sciences, Room 813

Abstract:

Electricity prices are high dimensional, serially dependent and have seasonal variations. We propose a Warping Functional AutoRegressive (WFAR) model that simultaneously accounts for the cross time-dependence and seasonal variations of the large dimensional data. In particular, electricity price curves are obtained by smoothing over the 24 discrete hourly prices on each day. In the functional domain, seasonal phase variations are separated from level amplitude changes in a warping process with the Fisher-Rao distance metric, and the aligned (season-adjusted) electricity price curves are modeled in the functional autoregression framework. In a real application, the WFAR model provides superior out-of-sample forecast accuracy in both a normal functioning market, Nord Pool, and an extreme situation, the California market. The forecast performance as well as the relative accuracy improvement are stable for different markets and different time periods.