Name
Abstract | ||
|---|---|---|
Accurate weather forecasting is one of most challenging tasks that deals with a large amount of observations and features. In this paper, a black-box modeling technique is proposed for temperature forecasting. Due to the high dimensionality of data, feature selection is done in two steps with k-Nearest Neighbors and Elastic net. Next, Least Squares Support Vector Machine regression is applied to generate the forecasting model. In the experimental results, the influence of each part of this procedure on the performance is investigated and compared with "Weather underground" results. For the case study, the prediction of the temperature in Brussels is considered. It is shown that black-box modeling has a good and competitive accuracy with current state-of-the-art methods for temperature prediction. |
Year | Venue | Field |
|---|---|---|
2015 | 2015 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) | Black box (phreaking),Data mining,Feature selection,Least squares support vector machine,Elastic net regularization,Computer science,Support vector machine,Curse of dimensionality,Artificial intelligence,Probabilistic forecasting,Weather forecasting,Machine learning |
DocType | ISSN | Citations |
Conference | 2161-4393 | 1 |
PageRank | References | Authors |
0.42 | 6 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zahra Karevan | 1 | 7 | 2.56 |
| Siamak Mehrkanoon | 2 | 103 | 11.90 |
| Johan A K Suykens | 3 | 2346 | 241.14 |