Name
Title | ||
|---|---|---|
Time Series Forecasting. A Comparative Study Between An Evolving Artificial Neural Networks System And Statistical Methods |
Abstract | ||
|---|---|---|
Accurate time series forecasting are important for displaying the manner in which the past continues to affect the future and for planning our day to-day activities. In recent years, a large literature has evolved on the use of evolving artificial neural networks (EANN) in many forecasting applications. Evolving neural networks are particularly appealing because of their ability to model an unspecified non-linear relationship between time series variables. In this work, a new approach of a previous Automatic Design of Artificial Neural Networks (ADANN) system applied to forecast time series is tackled. The automatic process to design artificial neural networks is carried out by a genetic algorithm (GA). These new methods, in order to get an accurate forecasting, are related with: shuffling training and validation patterns obtained from time series values and trying to improve the fitness function used in the global learning process (i.e. GA) using a new patterns set called validation II apart of the two used till the moment (i.e. training and validation). The object of this study is to try to improve the final forecasting getting an accurate system. In this paper, we also compare the forecasting ability of the ARIMA approach, evolving artificial neural networks (ADANN), unobserved components model (UCM) and a forecasting tool called Forecast Pro software using six benchmark time series. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1142/S0218213011000462 | INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS |
Keywords | Field | DocType |
Evolutionary computation, genetic algorithms, artificial neural networks, time series, forecasting, statistic | Time series,Computer science,Evolutionary computation,Fitness function,Autoregressive integrated moving average,Software,Shuffling,Artificial intelligence,Artificial neural network,Machine learning,Genetic algorithm | Journal |
Volume | Issue | ISSN |
21 | 1 | 0218-2130 |
Citations | PageRank | References |
5 | 0.41 | 10 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Juan Peralta | 1 | 83 | 6.56 |
| Germán Gutiérrez Sánchez | 2 | 56 | 2.59 |
| Araceli Sanchis de Miguel | 3 | 75 | 9.68 |