Evaluation of Differential Evolution and Particle Swarm Optimization Algorithms at Training of Neural Network for prediction

Abdelsalam, Mustafa; Ahmed, Hatem; Abdulwahed, W.

doi:10.21608/ijci.2014.33964

Cited by 4 publications

(5 citation statements)

References 9 publications

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“…Like the GA, DE utilizes the selection, crossover, and mutation operators. A significant difference in generating better solutions is that the GAs rely on the crossover operator whereas the DE algorithms rely on the mutation operation as the main operator (Abdual‐Salam, Abdul‐Kader, & Abdel‐Wahed, ). In DE, population size, crossover fraction, and generations are 80, 0.7, and 100, respectively.…”

Section: Proposed Methodsmentioning

confidence: 99%

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Comparative study of hybrid artificial neural network methods under stationary and nonstationary data in stock market

Dosdoğru

2019

Manage Decis Econ

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In this study, a new methodology is proposed to automatically determine six parameters of artificial neural network using population-based metaheuristics. We considered following three issues: What is the effect of used metaheuristic on performance? Which parameters are mostly selected? Is there a difference between the forecasting results when using stationary or nonstationary dataset that are selected according to the augmented Dickey-Fuller test statistics? Based upon results of performance measures, proposed method leads to significant opportunities to forecast stock market more effectively. We also expect proposed methodology can provide remarkable advantages for other complex, dynamic, and nonlinear forecasting problems. | INTRODUCTIONStock market is one of the most important investment ways due to the potential of earning higher returns. It is also considered as one of the key indicators of economic growth of countries. Over the years, researchers, practitioners, and investors analyzed the stock market, the stocks, and the relationships. However, stock market has high complexity and nonlinear constraints to grasp. Although anybody can own stocks to perform the aspired profit, many investors find stock market hard to detect which stock to buy, or when to buy and sell stocks. Furthermore, it is so difficult to exactly say when the stock market price will increase, how much it will increase or how long the increase of price will remain (Albeladi & Abdullah, 2018).Due to high volatilities and noises in stock market, unilateral traditional forecasting method and analysis cannot meet investors' need any more. Hence, new methods with recent advances in artificial intelligence have been proposed to extract useful information from stock market data. Depending on the nature of the stock market, a particular method can be used to gain profit. However, there is no single model exist in literature. Each method has its own strengths and limitations in terms of performance and reliability. In this case, artificial intelligence methods can be integrated to combine the strength of methods and overcome a single method's weakness. Especially, artificial neural network (ANN) method can be proposed for stock market forecasting in order to improve investors' decision support quality. In this paper, genetic algorithm (GA), differential evolution (DE), and particle swarm optimization (PSO) are integrated with ANN to clarify the dynamic and nonstationary nature of the stock market. GA, DE, and PSO are population-based metaheuristics. The group of populationbased metaheuristics inherently has some significant advantages. First, they ensure information about the "surface" of an objective function.Second, population-based metaheuristics are less sensitive to "improper" pathways of certain individual solutions. Finally, they improve the probability of attaining the global optimum (Feoktistov, 2006). This study involves the following tasks: (a) determine the relevant technical indicators for the stocks, (b) determine the proper transfe...

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Section: Proposed Methodsmentioning

confidence: 99%

“…PSO is derived from the behavior of social groups such as bird flocks or fish swarms (Abdual‐Salam et al, ). The basics of PSO can briefly be summarized as follows.…”

Section: Proposed Methodsmentioning

confidence: 99%

Comparative study of hybrid artificial neural network methods under stationary and nonstationary data in stock market

Dosdoğru

2019

Manage Decis Econ

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“…This influences the accuracy of the predicted results of ANN. To cope with this problem, a new method using the evolutionary algorithms is proposed in some researches [5], [12]- [14]. Kim and Han [15] used a genetic algorithm to transform continuous input values into discrete ones.…”

Section: Related Workmentioning

confidence: 99%

Forecasting Stock Price using Wavelet Neural Network Optimized by Directed Artificial Bee Colony Algorithm

Khuat,

Le,

Nguyen

et al. 2016

JTIT

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Stock prediction with data mining techniques is one of the most important issues in finance. This field has attracted great scientific interest and has become a crucial research area to provide a more precise prediction process. This study proposes an integrated approach where Haar wavelet transform and Artificial Neural Network optimized by Directed Artificial Bee Colony algorithm are combined for the stock price prediction. The proposed approach was tested on the historical price data collected from Yahoo Finance with different companies. Furthermore, the prediction result was found satisfactorily enough as a guide for traders and investors in making qualitative decisions.

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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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Evaluation of Differential Evolution and Particle Swarm Optimization Algorithms at Training of Neural Network for prediction

Cited by 4 publications

References 9 publications

Comparative study of hybrid artificial neural network methods under stationary and nonstationary data in stock market

Comparative study of hybrid artificial neural network methods under stationary and nonstationary data in stock market

Forecasting Stock Price using Wavelet Neural Network Optimized by Directed Artificial Bee Colony Algorithm

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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