Revenue forecasting using a least-squares support vector regression model in a fuzzy environment

Lin, Kuo-Ping; Pai, Ping-Feng; Lu, Y.; Chang, Ping-Teng

doi:10.1016/j.ins.2011.09.003

Cited by 73 publications

(25 citation statements)

References 85 publications

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“…LSSVM is an alternate formulation of SVM regression. In LSSVM, the inequality constraints are replaced with equality constraints; and a least squares linear system is adopted as a loss function instead of the time-consuming quadratic program in original SVM [25,26,29,33,43,44]. In the following, LSSVM algorithm is described briefly.…”

Section: Least Squares Support Vector Machine (Lssvm)mentioning

confidence: 99%

“…LSSVM adopts a least squares linear system as a loss function instead of the quadratic program in original SVM which is time consuming in training process [26][27][28][29][30]. The LSSVM shows manifest advantages, such as good nonlinear fitting ability, strong generalization capability, fast computing speed, dealing with small samples, not relying on the distribution characteristics of the samples and so on [31][32][33][34]. The performance of the LSSVM model is largely dependent on the selection of the parameters.…”

Section: Introductionmentioning

confidence: 99%

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Forecasting Fossil Fuel Energy Consumption for Power Generation Using QHSA-Based LSSVM Model

Sun

Hong

2015

Energies

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Accurate forecasting of fossil fuel energy consumption for power generation is important and fundamental for rational power energy planning in the electricity industry. The least squares support vector machine (LSSVM) is a powerful methodology for solving nonlinear forecasting issues with small samples. The key point is how to determine the appropriate parameters which have great effect on the performance of LSSVM model. In this paper, a novel hybrid quantum harmony search algorithm-based LSSVM (QHSA-LSSVM) energy forecasting model is proposed. The QHSA which combines the quantum computation theory and harmony search algorithm is applied to searching the optimal values of σ and C in LSSVM model to enhance the learning and generalization ability. The case study on annual fossil fuel energy consumption for power generation in China shows that the proposed model outperforms other four comparative models, namely regression, grey model (1, 1) (GM (1, 1)), back propagation (BP) and LSSVM, in terms of prediction accuracy and forecasting risk.

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Section: Least Squares Support Vector Machine (Lssvm)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Forecasting Fossil Fuel Energy Consumption for Power Generation Using QHSA-Based LSSVM Model

Sun

Hong

2015

Energies

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“…The second one is based on the support vector machine theory proposed by [34]. The latter has proved its efficiency for solving high nonlinear problems in many engineering applications [35,36,37,38,39]. It should be noted that the complexity of the proposed problem arises from the nonlinearity of the limit state function (LSF) which seperates the failure domain from the safe one which makes the evaluation of the probability of failure associated to each plastic instability a challenging task.…”

Section: Introductionmentioning

confidence: 99%

A probabilistic approach for optimising hydroformed structures using local surrogate models to control failures

Abdessalem

Hami²

2015

International Journal of Mechanical Sciences

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“…Recently, application of fuzzy numbers in data mining algorithms has been an interesting topic to the researchers in this domain, for instance, clustering [1,2], classification [3] and regression [4,5]. Generally, the efforts have been done in study of fuzzy mathematical analysis and its application falls into two main categories:…”

Section: Introductionmentioning

confidence: 99%

Homeomorphism Problems of Fuzzy Real Number Space and The Space of Bounded Functions with Same Monotonicity on [-1,1]

Wang¹,

Guo²,

Bamakan³

et al. 2015

INT J COMPUT COMMUN

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In this paper, based on the fuzzy structured element, we prove that there is a bijection function between the fuzzy number space ε 1 and the space B[−1, 1], which defined as a set of standard monotonic bounded functions with monotonicity on interval [−1, 1]. Furthermore, a new approach based upon the monotonic bounded functions has been proposed to create fuzzy numbers and represent them by suing fuzzy structured element. In order to make two different metrics based space in B[−1, 1], Hausdorff metric and L p metric, which both are classical functional metrics, are adopted and their topological properties are discussed. In addition, by the means of introducing fuzzy functional to space B[−1, 1], we present two new fuzzy number's metrics. Finally, according to the proof of homeomorphism between fuzzy number space ε 1 and the space B[−1, 1], it's argued that not only does it give a new way to study the fuzzy analysis theory, but also makes the study of fuzzy number space easier.

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Revenue forecasting using a least-squares support vector regression model in a fuzzy environment

Cited by 73 publications

References 85 publications

Forecasting Fossil Fuel Energy Consumption for Power Generation Using QHSA-Based LSSVM Model

Forecasting Fossil Fuel Energy Consumption for Power Generation Using QHSA-Based LSSVM Model

A probabilistic approach for optimising hydroformed structures using local surrogate models to control failures

Homeomorphism Problems of Fuzzy Real Number Space and The Space of Bounded Functions with Same Monotonicity on [-1,1]

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