Spatio-temporal feature selection for black-box weather forecasting
نویسنده
چکیده
In this paper, a data-driven modeling technique is proposed for temperature forecasting. Due to the high dimensionality, LASSO is used as feature selection approach. Considering spatio-temporal structure of the weather dataset, first LASSO is applied in a spatial and temporal scenario, independently. Next, a feature is included in the model if it is selected by both. Finally, Least Squares Support Vector Machines (LSSVM) regression is used to learn the model. The experimental results show that spatio-temporal LASSO improves the performance and is competitive with the state-of-the-art methods. As a case study, the prediction of the temperature in Brussels is considered.
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