A stock selection algorithm hybridizing grey wolf optimizer and support vector regression

Li, Meng; Luo, Kaiping; Zhang, Junhuan; Chen, Shengli

doi:10.1016/j.eswa.2021.115078

Cited by 52 publications

(22 citation statements)

References 51 publications

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“…Particle swarm optimization is a metaheuristics-based method of optimization that was inspired by fish training and bird swarming. The algorithm addresses optimization problems by considering a flock of birds with social interactions among themselves in a search for sources of food [33,34]. Each bird searching for food sources is considered a particle; the swarm refers to the flock of birds.…”

Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

Modeling the Optical Properties of a Polyvinyl Alcohol-Based Composite Using a Particle Swarm Optimized Support Vector Regression Algorithm

Owolabi

Rahman

2021

Polymers

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We developed particle swarm optimization-based support vector regression (PSVR) and ordinary linear regression (OLR) models for estimating the refractive index (n) and energy gap (E) of a polyvinyl alcohol composite. The n-PSVR model, which can estimate the refractive index of a polyvinyl alcohol composite using the energy gap as a descriptor, performed better than the n-OLR model in terms of root mean square error (RMSE) and mean absolute error (MAE) metrics. The E-PSVR model, which can predict the energy gap of a polyvinyl alcohol composite using its refractive index descriptor, outperformed the E-OLR model, which uses similar descriptor based on several performance measuring metrics. The n-PSVR and E-PSVR models were used to investigate the influences of sodium-based dysprosium oxide and benzoxazinone derivatives on the energy gaps of a polyvinyl alcohol polymer composite. The results agreed well with the measured values. The models had low mean absolute percentage errors after validation with external data. The precision demonstrated by these predictive models will enhance the tailoring of the optical properties of polyvinyl alcohol composites for the desired applications. Costs and experimental difficulties will be reduced.

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Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

Modeling the Optical Properties of a Polyvinyl Alcohol-Based Composite Using a Particle Swarm Optimized Support Vector Regression Algorithm

Owolabi

Rahman

2021

Polymers

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“…Considering the very complex and nonlinear fluctuations in the market price, we need to make use of a novel smart optimization algorithm based on grey wolf optimization (GWO). It is a heuristic optimization method which has shown great abilities over the other algorithms in the literature [24,25]. Therefore, the main contributions of the paper can be shown as below:…”

Section: Introductionmentioning

confidence: 99%

Management and Policy Modeling of the Market Using Artificial Intelligence

Fan

2022

Sustainability

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This paper investigates the market management and modeling based on advanced artificial intelligence. The proposed model deploys the combination of the support vector machine and fuzzy set theory to provide a practical and powerful prediction model for the market price over the next day. A realistic and effective model is then introduced to model the market players, such as the renewable energy sources of solar and wind turbines, as well as the fossil-fueled sources of micro turbines and fuel cells. In order to provide an optimal management program, it introduces a stochastic framework based on the point estimate method and adaptive grey wolf optimization algorithm (GWO). The proposed optimization methods use an adaptive strategy to choose the most fitting modification for enhancing the GWO performance. A realistic scenario is simulated to demonstrate the model’s effectiveness and impression on the real market management. The results clearly show the effectiveness of the prediction and management model. The prediction results show the superiority of the proposed model by RMSE of 2.9643 compared to the 3.217 for SVR, 3.2364 for ANN and 3.0621 for the grey model. Moreover, the optimal MAPE is 2.7453 by the proposed method, which is much better than the 3.052 by SVR, 3.1552 by ANN and 2.9285 by the grey model. From point of view of optimization, the most fitting power dispatch has been attained with the total cost of 300.8632 over 24 h.

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“…OSELM has been used in a variety of research fields, including power quality event detection (Sahani et al 2020), time series analysis (Das et al 2019), and stream flow forecasting (Lima et al 2017), and has outperformed basic ELM and other ML techniques as well. Several researchers have addressed the second issue by hybridizing the training of ELM using optimization techniques such as particle swarm optimization (PSO) (Pradeepkumar and Ravi 2017), harmony search (HS) (Dash et al 2014), grey wolf optimization (GWO) (Liu et al 2021), teachinglearning-based optimization (TLBO) (Das and Padhy 2018), crow search algorithm (CSA) (Dash et al 2021), differential evolution (DE) (Abdual-Salam et al 2010), and others. These models not only increase accuracy, but also enhance stability of the model.…”

Section: Introductionmentioning

confidence: 99%

Developing a novel stock index trend predictor model by integrating multiple criteria decision-making with an optimized online sequential extreme learning machine

Samal

Dash

2022

Granul. Comput.

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It has always been the goal of many researchers to gain a thorough understanding of the patterns in the stock market and forecast the trends it will follow. The use of an advanced forecasting model can assist with accurately forecasting the future price of stocks, their fluctuations in the markets, as well as make profits in trading. With this motivation, in this study, a novel stock index trend predictor model is designed by integrating Multiple Criteria Decision-Making (MCDM) with an optimized Online Sequential Extreme Learning Machine (OSELM). Forecasting the future stock index prices and analyzing the upward or downward trends of these price forecasts are the two objectives of the proposed model. As the performance of OSELM is heavily dependent on the activation functions used in it, suitable selection of the activation function for OSELM is addressed as a MCDM problem. According to this approach, the trend prediction performance of six popular activation functions is assessed based on five regression-based and five classification-based criteria. In this investigation, three MCDM approaches are used to assess the performance matrix and determine which activation function is the best for OSELM based on six alternative models and ten criteria. To further optimize OSELM's performance, a hybrid crow search algorithm (hCSA) is incorporated in its training phase. By introducing the chaotic map and mutation operator in position update scheme and catfish behavior in the search process of original CSA, the proposed hCSA is able to achieve the right balance between exploration and exploitation improving the convergence. The proposed trend predictor model is empirically evaluated over historical data of three stock indices such as BSE SENSEX, S&P 500 and DJIA collected during pre-COVID and COVID time frame. In most of the test cases, the hCSA-OSELM model outperforms the state-of-the-art baseline models in terms of all evaluation criteria. When compared to the second-best baseline model, the suggested model is able to achieve the MSE improvements of 4–6%, 25–31%, and accuracy improvements of 0.4–0.8%, 0.9–1.3% over the pre-COVID and COVID time-frames, respectively. The statistical test also reveals the better performance of the proposed model. The robust and reliable MCDM-based model selection, superior prediction and classification outcomes clearly reveal that the proposed model can be used for financial time-series forecasting amid daily volatility as well as highly volatile markets.

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A stock selection algorithm hybridizing grey wolf optimizer and support vector regression

Cited by 52 publications

References 51 publications

Modeling the Optical Properties of a Polyvinyl Alcohol-Based Composite Using a Particle Swarm Optimized Support Vector Regression Algorithm

Modeling the Optical Properties of a Polyvinyl Alcohol-Based Composite Using a Particle Swarm Optimized Support Vector Regression Algorithm

Management and Policy Modeling of the Market Using Artificial Intelligence

Developing a novel stock index trend predictor model by integrating multiple criteria decision-making with an optimized online sequential extreme learning machine

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