N. Kourentzes, 2011, International Conference on Data Mining, DMIN’2011, Las Vegas, 18-21 July 2011.
This paper discusses and empirically evaluates alternative methodologies in modeling functional outliers for high frequency time series forecasting. In spite of several modeling and forecasting methodologies that have been proposed, there have been limited advancements in monitoring and automatically identifying outlying patterns and even less in modeling those for such times series. This is a significant gap considering the difficulty and the cost associated with manual exploration and treatment of such data, due to the vast number of observations. This study proposes and assesses the performance of different modeling methodologies focusing on two key aspects, the accuracy that the outliers are modeled and the impact of each methodology on modeling normal observations. The evaluated methodologies model functional outliers using binary, integer or trigonometric dummy variables, outlier profiles or isolate them into new time series and forecast them separately. Neural networks are employed to produce the forecasts, taking advantage of their flexible nature to accommodate the different methodologies and their superior performance in high frequency time series forecasting. Hourly electricity load data from the UK are used to empirically evaluate the performance of the different methodologies.