Name
Title | ||
|---|---|---|
Time series forecasting using a weighted cross-validation evolutionary artificial neural network ensemble |
Abstract | ||
|---|---|---|
The ability to forecast the future based on past data is a key tool to support individual and organizational decision making. In particular, the goal of Time Series Forecasting (TSF) is to predict the behavior of complex systems by looking only at past patterns of the same phenomenon. In recent years, several works in the literature have adopted Evolutionary Artificial Neural Networks (EANNs) for TSF. In this work, we propose a novel EANN approach, where a weighted n-fold validation fitness scheme is used to build an ensemble of neural networks, under four different combination methods: mean, median, softmax and rank-based. Several experiments were held, using six real-world time series with different characteristics and from distinct domains. Overall, the proposed approach achieved competitive results when compared with a non-weighted n-fold EANN ensemble, the simpler 0-fold EANN and also the popular Holt-Winters statistical method. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1016/j.neucom.2012.02.053 | Neurocomputing |
Keywords | Field | DocType |
different combination method,cross-validation evolutionary artificial neural,different characteristic,evolutionary artificial neural networks,past data,simpler 0-fold eann,non-weighted n-fold eann ensemble,network ensemble,past pattern,time series forecasting,weighted n-fold validation fitness,novel eann approach,genetic algorithms,evolutionary computation,multilayer perceptron | Complex system,Time series,Softmax function,Computer science,Evolutionary computation,Multilayer perceptron,Artificial intelligence,Artificial neural network,Cross-validation,Genetic algorithm,Machine learning | Journal |
Volume | ISSN | Citations |
109, | 0925-2312 | 18 |
PageRank | References | Authors |
0.74 | 15 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Juan Peralta | 1 | 83 | 6.56 |
| Paulo Cortez | 2 | 157 | 12.29 |
| Germán Gutiérrez Sánchez | 3 | 56 | 2.59 |
| Araceli Sanchis de Miguel | 4 | 75 | 9.68 |
| DonateJuan Peralta | 5 | 18 | 0.74 |
| SáNchezGermáN GutiéRrez | 6 | 18 | 0.74 |
| De MiguelAraceli Sanchis | 7 | 18 | 0.74 |