A Comparative Study on Feature Selection based Improvement of Medium-Term Demand Forecast Accuracy

Ilseven, Engin
Göl, Murat
Use of the proper demand forecasting method and data set is very important for reliable system operation and planning. In this study, we compare performances of various feature selection method forecasting algorithm pairs in terms of forecast accuracy for medium-term demand forecasting case. We utilize correlation, recursive feature elimination, random forest, multivariate adaptive regression splines (MARS), stepwise regression and genetic algorithms as feature selection methods. As for forecasting algorithms, we use linear and non-linear methods such as multiple linear regression (MLR), generalized additive model (GAM), MARS, k-nearest neighbors (KNN), classification and regression trees (CART), neural networks (NN) and support vector machines (SVM) methods. In the end, the MARS and stepwise regression as feature selection methods, and MARS model as the forecasting algorithm are found to be showing superior performance. However, with a suitable feature selection technique such as stepwise regression, linear models can yield satisfactory level of performance considering their low computation requirement.


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Multivariate Adaptive Regression Splines (MARS) is a very popular nonparametric regression method particularly useful for modeling nonlinear relationships that may exist among the variables. Recently, we developed CMARS method as an alternative to backward stepwise part of the MARS algorithm. Comparative studies have indicated that CMARS performs better than MARS for modeling nonlinear relationships. In those studies, however, only main and two-factor interaction effects were sufficient to model the nonline...
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Bootstrapping is a computer-intensive statistical method which treats the data set as a population and draws samples from it with replacement. This resampling method has wide application areas especially in mathematically intractable problems. In this study, it is used to obtain the empirical distributions of the parameters to determine whether they are statistically significant or not in a special case of nonparametric regression, conic multivariate adaptive regression splines (CMARS), a statistical machin...
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Medium-Term Electricity Demand Forecasting Based on MARS
İlseven, Engin; Göl, Murat (2017-09-29)
The paper proposes use of multivariate adaptive regression splines (MARS) method to perform monthly electricity demand forecasting for medium-term. The model is developed based on specific example of Turkey; however is applicable to any other system. Performance of the proposed method is compared to that of multiple linear regression (MLR), generalized additive model (GAM), and artificial neural networks (ANN) methods. The validation process shows that the proposed model outperforms the other ones by test e...
Citation Formats
E. Ilseven and M. Göl, “A Comparative Study on Feature Selection based Improvement of Medium-Term Demand Forecast Accuracy,” 2019, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/37410.