Abstract
We consider feature selection for interval forecasting of time series data. In particular, we study feature selection for LUBEX, a neural-network based approach for computing prediction intervals and its application for predicting future electricity demands from a time series of previous demands. Our results show that the mutual information and correlation-based feature selection methods are able to select a small set of lag variables that when used with LUBEX construct valid and stable prediction intervals (coverage probability of 97.44% and 96.68%, respectively, for confidence level of 90%). In contrast, the popular partial autocorrelation feature selection method fails to do this (coverage probability of 69.69%). Our evaluation was conducted using one year of half-hourly Australian electricity demand data.
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Rana, M., Koprinska, I., Khosravi, A. (2013). Feature Selection for Neural Network-Based Interval Forecasting of Electricity Demand Data. In: Mladenov, V., Koprinkova-Hristova, P., Palm, G., Villa, A.E.P., Appollini, B., Kasabov, N. (eds) Artificial Neural Networks and Machine Learning – ICANN 2013. ICANN 2013. Lecture Notes in Computer Science, vol 8131. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40728-4_49
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DOI: https://doi.org/10.1007/978-3-642-40728-4_49
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