Coevolution of lags and RBFNs for time series forecasting: L-Co-R algorithm

Parras-Gutierrez, Elisabet; Arenas, M. G.; Rivas, Víctor M.; Jesus, María José del

doi:10.1007/s00500-011-0784-2

Cited by 10 publications

(6 citation statements)

References 82 publications

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“…As in (Parras-Gutierrez et al, 2012), the experimentation has been carried out using 20 data bases, most of then taken from the INE 1 . The data represent observations from different activities and have different nature, size, and characteristics.…”

Section: Experimental Methodologymentioning

confidence: 99%

“…The L-Co-R method (Parras-Gutierrez et al, 2012), developed inside the field of ANNs, makes jointly use of Radial Basis Function Networks (RBFNs) and EAs to automatically forecast any given time series. Moreover, L-Co-R designs adequate neural networks and selects the time lags that will be used in the prediction, in a coevolutive (Castillo et al, 2003) approach that allows to separate the main problem in two dependent subproblems.…”

Section: Introductionmentioning

confidence: 99%

“…While previously work (Parras-Gutierrez et al, 2012) was focused on 1-step ahead prediction, the main goal of this one is to analyze the effectiveness of the L-Co-R method in the medium-term horizon, using the own previously predicted values to perform next predictions. For this reason, L-Co-R has been tested over 20 databases, taken from real world, or used in well-known research publications and time series competition.…”

Section: Introductionmentioning

confidence: 99%

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The L-Co-R Co-evolutionary Algorithm - A Comparative Analysis in Medium-term Time-series Forecasting Problems

Parras-Gutierrez

Rivas

Merelo

2013

Proceedings of the 5th International Joint Conference on Computational Intelligence

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This paper presents an experimental study in which the effectiveness of the L-Co-R method is tested. L-Co-R is a co-evolutionary algorithm to time series forecasting that evolves, on one hand, RBFNs building an appropriate architecture of net, and on the other hand, sets of time lags that represents the time series in order to perform the forecasting using, at the same time, its own forecasted values. This coevolutive approach makes possible to divide the main problem into two subproblems where every individual of one population cooperates with the individuals of the other. The goal of this work is to analyze the results obtained by L-Co-R comparing with other methods from the time series forecasting field. For that, 20 time series and 5 different methods found in the literature have been selected, and 3 distinct quality measures have been used to show the results. Finally, a statistical study confirms the good results of L-Co-R in most cases.

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Section: Experimental Methodologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The L-Co-R Co-evolutionary Algorithm - A Comparative Analysis in Medium-term Time-series Forecasting Problems

Parras-Gutierrez

Rivas

Merelo

2013

Proceedings of the 5th International Joint Conference on Computational Intelligence

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“…Based on such an idea, a hybrid approach, integrating different technologies in a systematical way, can effectively take advantages of the respective merits of single models and offset the disadvantages [40]. Recent researches have repeatedly observed that hybrid models are significantly more efficient in prediction capability than single ones [12][13][14][15]17], and some current interesting works can refer to the hybrid L-Co-R algorithm (lags coevolving with radial basis function neural networks-RBFNs) [41,42] and hybrid decomposition-and-ensemble models (coupling decomposition and forecasting techniques) [13,15]. Therefore, this paper tries to formulate a novel hybrid searching algorithm for the parameter selection in the LSSVR by integrating the grid method and the GA, to guarantee the prediction accuracy for crude oil price forecasting.…”

Section: Literature Reviewmentioning

confidence: 99%

A hybrid grid-GA-based LSSVR learning paradigm for crude oil price forecasting

Dai

Tang

et al. 2015

Neural Comput & Applic

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In order to effectively model crude oil spot price with inherently high complexity, a hybrid learning paradigm integrating least squares support vector regression (LSSVR) with a hybrid optimization searching approach for the parameters selection in the LSSVR [consisting of grid method and genetic algorithm (GA)], i.e., a hybrid grid-GA-based LSSVR model, is proposed in this study. In the proposed hybrid learning paradigm, the grid method, a simple but efficient searching method, is first applied to roughly but rapidly determine the proper boundaries of the parameters in the LSSVR; then, the GA, an effective and powerful intelligent searching algorithm, is further implemented to select the most suitable parameters. For illustration and verification, the proposed learning paradigm is used to predict the crude oil spot prices of the West Texas Intermediate and the Brent markets. The empirical results demonstrate that the proposed hybrid grid-GA-based LSSVR learning paradigm can outperform its benchmarking models (including some popular forecasting techniques and similar LSSVRs with other parameter searching algorithms) in terms of both prediction accuracy and time-savings, indicating that it can be utilized as one effective forecasting tool for crude oil price with high volatility and irregularity.

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“…When adapting SVM to TSF, variable and model selection are critical issues [6] [23] [15]. Variable selection is useful to select the relevant time lags to be fed into the SVM.…”

mentioning

confidence: 99%

Global and decomposition evolutionary support vector machine approaches for time series forecasting

Cortez

Donate

2014

Neural Comput & Applic

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Multi-step ahead Time Series Forecasting (TSF) is a key tool for supporting tactical decisions (e.g., planning resources). Recently, the support vector machine emerged as a natural solution for TSF due to its nonlinear learning capabilities. This paper presents two novel Evolutionary Support Vector Machine (ESVM) methods for multi-step TSF. Both methods are based on an Estimation Distribution Algorithm (EDA) search engine that automatically performs a simultaneous variable (number of inputs) and model (hyperparameters) selection. The Global ESVM (GESVM) uses all past patterns to fit the support vector machine, while the Decomposition ESVM (DESVM) separates the series into trended and stationary effects, using a distinct ESVM to forecast each effect and then summing both predictions into a single response. Several experiments were held, using six time series. The proposed approaches were analyzed under two criteria and compared against a recent Evolutionary Artificial Neural Network (EANN) and two classical forecasting methods, Holt-Winters and ARIMA. Overall, the DESVM and GESVM obtained competitive and high quality results. Furthermore, both ESVM approaches consume much less computational effort when compared with EANN.

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Coevolution of lags and RBFNs for time series forecasting: L-Co-R algorithm

Cited by 10 publications

References 82 publications

The L-Co-R Co-evolutionary Algorithm - A Comparative Analysis in Medium-term Time-series Forecasting Problems

The L-Co-R Co-evolutionary Algorithm - A Comparative Analysis in Medium-term Time-series Forecasting Problems

A hybrid grid-GA-based LSSVR learning paradigm for crude oil price forecasting

Global and decomposition evolutionary support vector machine approaches for time series forecasting

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