Name
Abstract | ||
|---|---|---|
Time Series Forecasting (TSF) uses past patterns of an event in order to predict its future values and is a key tool to support decision making. In the last decades, Computational Intelligence (CI) techniques, such as Artificial Neural Networks (ANN) and more recently Support Vector Machines (SVM), have been proposed for TSF. The accuracy of the best CI model is affected by both the selection of input time lags and the model's hyperparameters. In this work, we propose a novel Evolutionary SVM (ESVM) approach for TSF based on the Estimation Distribution Algorithm to search for the best number of inputs and SVM hyperparameters. Several experiments were held, using a set of six time series from distinct real-world domains. Overall, the proposed ESVM is competitive when compared with an Evolutionary ANN (EANN) and the popular ARIMA methodology, while consuming less computational effort when compared with EANN. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-33266-1_65 | ICANN (2) |
Keywords | Field | DocType |
evolutionary support vector machine,time series,best number,computational intelligence,input time lag,ci model,novel evolutionary svm,artificial neural networks,svm hyperparameters,time series forecasting,evolutionary ann,proposed esvm,support vector machines,forecasting,evolutionary computation | Time series,Computational intelligence,Hyperparameter,Estimation of distribution algorithm,Computer science,Support vector machine,Evolutionary computation,Autoregressive integrated moving average,Artificial intelligence,Artificial neural network,Machine learning | Conference |
Citations | PageRank | References |
0 | 0.34 | 9 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Paulo Cortez | 1 | 15 | 6.45 |
| Juan Peralta | 2 | 83 | 6.56 |