Enhanced artificial bee colony for training least squares support vector machines in commodity price forecasting

Mustaffa, Zuriani; Yusof, Yuhanis; Kamaruddin, Siti Sakira

doi:10.1016/j.jocs.2013.11.004

Cited by 37 publications

(24 citation statements)

References 13 publications

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“…al., [8], have reported empirical results that examine the feasibility of eABC-LSSVM in predicting prices of the time series of interest. The performance of their proposed prediction model was evaluated using four statistical metric, namely MAPE, PA, SMAPE and RMSPE and experimented using three different set of data arrangement, in order to choose the best data arrangement for generalization purposes.…”

Section: Literature Reviewmentioning

confidence: 99%

Enhancing Filter Based Algorithms for Selecting Optimal Features From Thyroid Disease Dataset

Pavya¹

2017

ijarcs

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Abstract:In medical science, automatic disease diagnosis is an invaluable tool because of restricted observation of the specialist and uncertainties in medical knowledge. Advances in medical information technology have enabled healthcare industries to automatically collect huge amount of data through clinical laboratory examinations. To explore these data, the past few years have envisaged the use of Computer Aided Diagnosis (CAD) systems in many screening sites and hospitals. While using CAD, thyroid function diagnosis is considered as a classification problem, which can automatically identify the type of thyroid (hyper, hypo or normal). Machine learning techniques are increasingly introduced to construct the CAD systems owing to its strong capability of extracting complex relationships in the biomedical data.

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Section: Literature Reviewmentioning

confidence: 99%

Enhancing Filter Based Algorithms for Selecting Optimal Features From Thyroid Disease Dataset

Pavya¹

2017

ijarcs

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“…A study on the prediction of nonrenewable commodity such as crude oil and gold has been reported in 2011using the ABC-LSSVM [64]. Results showed that the ABC-LSSVM possess higher accuracy than existing prediction models that includes Backpropogation Neural Network (BPNN) and Differential Evolution -Least Squares Support Vector Machines (DE-LSSVM).The work was later extended by the researchers to improve the search mechanism of ABC and this was presented as eABC-LSSVM [65,66]. The extension includes two sub algorithms termed as lvABC-LSSVM [67] and cmABC-LSSVM.…”

Section: Optimization Of Lssvm Using Evolutionary Computationmentioning

confidence: 99%

A Review on Optimization of Least Squares Support Vector Machine for Time Series Forecasting

Yusof¹,

Mustaffa²

2016

IJAIA

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Support Vector Machine has appeared as an active study in machine learning community and extensively used in various fields including in prediction, pattern recognition and many more. However, the Least Squares Support Vector Machine which is a variant of Support Vector Machine offers better solution strategy. In order to utilize the LSSVM capability in data mining task such as prediction, there is a need to optimize its hyper parameters. This paper presents a review on techniques used to optimize the parameters based on two main classes; Evolutionary Computation and Cross Validation.

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“…The forecast accuracy of these models has been improved by optimizing their input with evolutionary search heuristics, such as Particle Swarm Optimization [4, 32,60,62,63], Genetic Algorithms [22,30,39,42,43,49,54], Simulated Annealing [23,40], Artificial Bee Colony Algorithm [5, 24,47], Differential Evolution [25,57] and Fruit Fly Optimization [38,41]. These hybrid methodologies have been applied to many different fields in forecasting, including tourism flow forecasting [14], electricity demand forecasting [63], rainfall prediction [60], price forecasting [47] and many others. The literature on evolutionary algorithms for AR(I)MA modeling mainly focuses on Genetic Algorithms [1, 21,29,45,49], with a few exceptions, such as [26].…”

Section: Introductionmentioning

confidence: 99%

“…Especially the AIC value is often used when manually selecting the parameters of an AR(I)MA model [19,65]. However, the most popular criterion for optimization in forecasting is accuracy, which can take many forms, such as the Mean Squared Error (MSE) [1,4,5,30,34,54,62], the Mean Absolute Percentage Error (MAPE) [14,23,38,39,42,47,57,63] or the Root Mean Squared Error (RMSE) [22,25,27,38]. In this paper, however, we turn to a profit measure for sales forecasting to optimize the order identification of Seasonal ARIMA models.…”

Section: Introductionmentioning

confidence: 99%

ProfARIMA: A profit-driven order identification algorithm for ARIMA models in sales forecasting

Calster

Baesens

Lemahieu

2017

Applied Soft Computing

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In forecasting, evolutionary algorithms are often linked to existing forecasting methods to optimize their input parameters. Traditionally, the fitness function of these search heuristics is based on an accuracy measure. In this paper, however, we combine forecasting accuracy with business expertise by defining a flexible and easily interpretable profit function for sales forecasting, which is based on the profit margin of a given product, the volume of its sales and the accuracy of the forecast. ProfARIMA is a new procedure that selects the lags of a Seasonal ARIMA model according to the profit of a model's forecasts by taking advantage of search heuristics. This procedure is tested on both publicly available datasets and a real-life application with datasets of The Coca-Cola Company in order to assess its performance, both in profit and accuracy. Three different evolutionary algorithms were implemented during this testing process, i.e. Genetic Algorithms, Particle Swarm Optimization and Simulated Annealing. The results indicate that ProfARIMA always performs at least equally to the Box-Jenkins methodology and often outperforms this traditional procedure. For The Coca-Cola Company, our new algorithm in combination with Genetic Algorithms even leads to a significantly larger profit for out-of-sample forecasts.

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Enhanced artificial bee colony for training least squares support vector machines in commodity price forecasting

Cited by 37 publications

References 13 publications

Enhancing Filter Based Algorithms for Selecting Optimal Features From Thyroid Disease Dataset

Enhancing Filter Based Algorithms for Selecting Optimal Features From Thyroid Disease Dataset

A Review on Optimization of Least Squares Support Vector Machine for Time Series Forecasting

ProfARIMA: A profit-driven order identification algorithm for ARIMA models in sales forecasting

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