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来源:统计系  作者:统计系     日期:2017/9/5 11:15:46   点击数:380  

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

时间: 2017925日,星期一上午10:00AM

地点: X2511 


简介: 张博士于2014年本科毕业于武汉大学数学与统计学院,同年获得新加坡政府全额奖学金资助的直博研究生资格,现为新加坡国立大学的统计学博士,师从Chen Ying教授。研究方向包括functional data analysis ; time warping; network modelling; large dimensional VAR model with sparsity


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 real application in both the California power market and the Nord Pool power market, the WFAR model provides superior out-of-sample forecasts with forecast error reduced significantly compared to several alternative models.