Research on Aesthetic Education in School Physical Education Based on Multiple Linear Regression Method

Shen, Yang; Ying, Huang; Luan, Qiang

doi:10.1007/978-1-4471-4844-9_73

Cited by 1 publication

(1 citation statement)

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“…Developing sales growth rate forecasting model is nontrivial due to its uncertain, nonlinear, dynamic, and complicated characteristics. Some recent and most commonly used forecasting models are neural network based prediction model [1], multiple linear regression analysis model [2], and grey forecasting model [3]. However, these models have their own weaknesses.…”

Section: Introductionmentioning

confidence: 99%

Sales Growth Rate Forecasting Using Improved PSO and SVM

Wang

Wen

Alam

et al. 2014

Mathematical Problems in Engineering

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Accurate forecast of the sales growth rate plays a decisive role in determining the amount of advertising investment. In this study, we present a preclassification and later regression based method optimized by improved particle swarm optimization (IPSO) for sales growth rate forecasting. We use support vector machine (SVM) as a classification model. The nonlinear relationship in sales growth rate forecasting is efficiently represented by SVM, while IPSO is optimizing the training parameters of SVM. IPSO addresses issues of traditional PSO, such as relapsing into local optimum, slow convergence speed, and low convergence precision in the later evolution. We performed two experiments; firstly, three classic benchmark functions are used to verify the validity of the IPSO algorithm against PSO. Having shown IPSO outperform PSO in convergence speed, precision, and escaping local optima, in our second experiment, we apply IPSO to the proposed model. The sales growth rate forecasting cases are used to testify the forecasting performance of proposed model. According to the requirements and industry knowledge, the sample data was first classified to obtain types of the test samples. Next, the values of the test samples were forecast using the SVM regression algorithm. The experimental results demonstrate that the proposed model has good forecasting performance.

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Section: Introductionmentioning

confidence: 99%