Forecasting Model Based on LSSVM and ABC for Natural Resource Commodity

Yusof, Yuhanis; Kamaruddin, Siti Sakira; Husni, Husniza; Ku-Mahamud, Ku Ruhana; Mustaffa, Zuriani

doi:10.7763/ijcte.2013.v5.820

Cited by 7 publications

(7 citation statements)

References 27 publications

(23 reference statements)

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“…The purpose of this comparison is to evaluate and determine which combination yielded the highest accuracy. Performance is measured using five metrics; MAPE (Yusof et al, 2015) as shown in Equation 13, accuracy (Yusof, Kamaruddin, Husni, Ku-Mahamud, & Mustaffa, 2013) as shown in Equation 14, SMAPE (Soliman & Salam, 2014) as shown in Equation 15 and RMSPE (Mustaffa et al, 2018) as shown in Equation 16, and best fitness as shown in Equation 17.…”

Section: Proposed Methodsmentioning

confidence: 99%

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A Hybrid Least Squares Support Vector Machine With Bat and Cuckoo Search Algorithms for Time Series Forecasting

Mohammed

Ghathwan

Yusof

2020

Journal of Information and Communication Technology

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Least Squares Support Vector Machine (LSSVM) has been known to be one of the effective forecasting models. However, its operation relies on two important parameters (regularization and kernel). Predetermining the values of parameters will affect the results of the forecasting model; hence, to find the optimal value of these parameters, this study investigates the adaptation of Bat and Cuckoo Search algorithms to optimize LSSVM parameters. Even though Cuckoo Search has been proven to be able to solve global optimization in various areas, the algorithm leads to a slow convergence rate when the step size is large. Hence, to enhance the search ability of Cuckoo Search, it is integrated with Bat algorithm that offers a balanced search between global and local. Evaluation was performed separately to further analyze the strength of Bat and Cuckoo Search to optimize LSSVM parameters. Five evaluation metrics were utilized; mean average percent error (MAPE), accuracy, symmetric mean absolute percent error (SMAPE), root mean square percent error (RMSPE) and fitness value. Experimental results on diabetes forecasting demonstrated that the proposed

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Section: Proposed Methodsmentioning

confidence: 99%

“…Equation 13, accuracy (Yusof, Kamaruddin, Husni, Ku-Mahamud, & Mustaffa, 2013) as shown Equation 14, SMAPE (Soliman & Salam, 2014) as shown in Equation 15 and RMSPE (Mustaffa et 2018) as shown in Equation 16, and best fitness as shown in Equation 17.…”

Section: Bat-lssvmmentioning

confidence: 99%

A Hybrid Least Squares Support Vector Machine With Bat and Cuckoo Search Algorithms for Time Series Forecasting

Mohammed

Ghathwan

Yusof

2020

Journal of Information and Communication Technology

Self Cite

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“…Long et al [29] proposed a modified ant colony optimization (ACO) algorithm for optimizing LSSVM parameters and applied the model of ACO-LSSVM to predict the short-term electrical power load. Yusof et al [30] proposed a prediction model by LSSVM and Artificial Bee Colony for gold prices. Mustaffa et al [31] proposed a novel hybrid prediction model of Grey Wolf Optimization (GWO) and LSSVM for realizing gold price forecasting, which was proven to make good predictions.…”

Section: Introductionmentioning

confidence: 99%

Forecasting of Energy Consumption in China Based on Ensemble Empirical Mode Decomposition and Least Squares Support Vector Machine Optimized by Improved Shuffled Frog Leaping Algorithm

Dai

Niu

2018

Applied Sciences

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For social development, energy is a crucial material whose consumption affects the stable and sustained development of the natural environment and economy. Currently, China has become the largest energy consumer in the world. Therefore, establishing an appropriate energy consumption prediction model and accurately forecasting energy consumption in China have practical significance, and can provide a scientific basis for China to formulate a reasonable energy production plan and energy-saving and emissions-reduction-related policies to boost sustainable development. For forecasting the energy consumption in China accurately, considering the main driving factors of energy consumption, a novel model, EEMD-ISFLA-LSSVM (Ensemble Empirical Mode Decomposition and Least Squares Support Vector Machine Optimized by Improved Shuffled Frog Leaping Algorithm), is proposed in this article. The prediction accuracy of energy consumption is influenced by various factors. In this article, first considering population, GDP (Gross Domestic Product), industrial structure (the proportion of the second industry added value), energy consumption structure, energy intensity, carbon emissions intensity, total imports and exports and other influencing factors of energy consumption, the main driving factors of energy consumption are screened as the model input according to the sorting of grey relational degrees to realize feature dimension reduction. Then, the original energy consumption sequence of China is decomposed into multiple subsequences by Ensemble Empirical Mode Decomposition for de-noising. Next, the ISFLA-LSSVM (Least Squares Support Vector Machine Optimized by Improved Shuffled Frog Leaping Algorithm) model is adopted to forecast each subsequence, and the prediction sequences are reconstructed to obtain the forecasting result. After that, the data from 1990 to 2009 are taken as the training set, and the data from 2010 to 2016 are taken as the test set to make an empirical analysis for energy consumption prediction. Four models, ISFLA-LSSVM, SFLA-LSSVM (Least Squares Support Vector Machine Optimized by Shuffled Frog Leaping Algorithm), LSSVM (Least Squares Support Vector Machine), and BP (Back Propagation) neural network (Back Propagation neural network), are selected to compare with the EEMD-ISFLA-LSSVM model based on the evaluation indicators of mean absolute percentage error (MAPE), root mean square error (RMSE), and mean absolute error (MAE), which fully prove the practicability of the EEMD-ISFLA-LSSVM model for energy consumption forecasting in China. Finally, the EEMD-ISFLA-LSSVM model is adopted to forecast the energy consumption in China from 2018 to 2022, and, according to the forecasting results, it can be seen that China's energy consumption from 2018 to 2022 will have a trend of significant growth.

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“…They used a time series analysis prediction method to calculate seasonal factors combined with a ACO-LSSVM algorithm to model the failure rate of an aviation device and obtained good experimental results. Yusof et al [19] proposed a forecasting model based on an optimized Least Squares Support Vector Machine. The determination of hyper-parameters is performed using a nature-inspired algorithm, the Artificial Bee Colony.…”

Section: Introductionmentioning

confidence: 99%

“…When we use the least squares support vector machine to forecast the load, the regularization parameter and the radial basis kernel function parameter need to be set, which directly influence the forecasting accuracy. Therefore, the researchers use a variety of methods to optimize the parameters of LSSVM, such as genetic algorithm (GA) [17], ant colony optimization algorithm (ACO) [18], artificial bee colony algorithm (ABC) [19], differential evolution algorithm (DE) [20], bat algorithm(BA) [21], principal component analysis [22]. Mahjoob et al [17] used a least-squares support vector machines approach, in combination with a hybrid genetic algorithm based optimization to forecast market clearing prices in two different power markets.…”

Section: Introductionmentioning

confidence: 99%

A Short-Term Load Forecasting Model with a Modified Particle Swarm Optimization Algorithm and Least Squares Support Vector Machine Based on the Denoising Method of Empirical Mode Decomposition and Grey Relational Analysis

Niu

Dai

2017

Energies

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Abstract:As an important part of power system planning and the basis of economic operation of power systems, the main work of power load forecasting is to predict the time distribution and spatial distribution of future power loads. The accuracy of load forecasting will directly influence the reliability of the power system. In this paper, a novel short-term Empirical Mode Decomposition-Grey Relational Analysis-Modified Particle Swarm Optimization-Least Squares Support Vector Machine (EMD-GRA-MPSO-LSSVM) load forecasting model is proposed. The model uses the de-noising method combining empirical mode decomposition and grey relational analysis to process the original load series and forecasts the processed subsequences by the algorithm of modified particle swarm optimization and least square support vector machine. Then, the final forecasting results can be obtained after reconstructing the forecasting series. This paper takes the Jibei area as an example to produce an empirical analysis for load forecasting. The model input includes the hourly load one week before the forecasting day and the daily maximum temperature, daily minimum temperature, daily average temperature, relative humidity, wind force, date type of the forecasting day. The model output is the hourly load of the forecasting day. The models of BP neural network, SVM (Support vector machine), LSSVM (Least squares support vector machine), PSO-LSSVM (Particle swarm optimization-Least squares support vector machine), MPSO-LSSVM (Modified particle swarm optimization-Least squares support vector machine), EMD-MPSO-LSSVM are selected to compare with the model of EMD-GRA-MPSO-LSSVM using the same sample. The comparison results verify that the short-term load forecasting model of EMD-GRA-MPSO-LSSVM proposed in this paper is superior to other models and has strong generalization ability and robustness. It can achieve good forecasting effect with high forecasting accuracy, providing a new idea and reference for accurate short-term load forecasting.

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Forecasting Model Based on LSSVM and ABC for Natural Resource Commodity

Cited by 7 publications

References 27 publications

A Hybrid Least Squares Support Vector Machine With Bat and Cuckoo Search Algorithms for Time Series Forecasting

A Hybrid Least Squares Support Vector Machine With Bat and Cuckoo Search Algorithms for Time Series Forecasting

Forecasting of Energy Consumption in China Based on Ensemble Empirical Mode Decomposition and Least Squares Support Vector Machine Optimized by Improved Shuffled Frog Leaping Algorithm

A Short-Term Load Forecasting Model with a Modified Particle Swarm Optimization Algorithm and Least Squares Support Vector Machine Based on the Denoising Method of Empirical Mode Decomposition and Grey Relational Analysis

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