AutoKeras and particle swarm optimization to predict the price trend of stock exchange

Fattah, Doaa A.; Naim, Amany A.; Desuky, Abeer S.; Zaki, Mervat S.

doi:10.11591/eei.v11i2.3373

Cited by 4 publications

(4 citation statements)

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“…Cognitive learning seeks the best position of a particle, whereas social learning seeks the best position of all particles in a swarm. Cognitive and social learning parameters are used to calculate the particle's speed and the speed of calculating the position of the next particle [20]. PSO algorithm steps [21]: i) assign random positions to all initial particles, ii) calculating the weights of the criteria (inertia, cognitive, and social), and iii) the number of repetitions for each particle is the process of finding the best solution.…”

Section: Particle Swam Optimizationmentioning

confidence: 99%

An efficiency metaheuristic model to predicting customers churn in the business market with machine learning-based

Y. Syah,

Muliono,

Akbar Siregar

et al. 2024

IJ-AI

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Metaheuristics is an optimization method that improves and completes a task in a short period of time based on its objective function. The goal of metaheuristics is to search the search space for the best solution. Machine learning detects patterns in large amounts of data. Machine learning encourages enterprise automation in a variety of areas in order to improve predictive ability without requiring explicit programming to make decisions. The percentage of customers who leave the company or stop using the service is referred to as churn. The purpose of this research is to forecast customer churn in the market business. Particle swam optimization (PSO) was used in this study as a metaheuristic method to provide a strategy to guide the search process for new customers and obtain parameters for processing by support vector regression (SVR). SVR predicts the value of a continuous variable by determining the best decision line to find the best value. The number of transactions, the number of periods, and the conversion value are the parameters that are visible. Efficiency models are added to improve prediction results through two optimizations: prediction flexibility and risk minimization. The findings demonstrate the effectiveness of prediction in reducing customer churn.

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Section: Particle Swam Optimizationmentioning

confidence: 99%

An efficiency metaheuristic model to predicting customers churn in the business market with machine learning-based

Y. Syah,

Muliono,

Akbar Siregar

et al. 2024

IJ-AI

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“…Based on the results produced, the analysis gained more than 60% accuracy for textual analysis (financial information) and 90% for numerical analysis (historical price data). Fattah et al [17] used deep neural network (DNN) to predict whether the closing price is reached at the profit which is determined by the investor or not and improve the accuracy of the prediction. Particle swarm optimization (PSO) and machine learning (AutoML) are used as optimizers with DNNs.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Forex prediction model using multiple regression, simulated annealing, reinforcement learning and technical analysis

Jamali

Chihab

García‐Magariño

et al. 2023

IJ-AI

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Foreign exchange market refers to the market in which currencies from around the world are traded. It allows investors to buy or sell a currency of their choice. Forex interests several categories of stakeholders, such as companies that carry out international contracts, large institutional investors, via the main banks, which carry out transactions on this market for speculative purposes. One of the most important aspects in the Forex market is knowing when to invest by buying, selling, and this through the recorded trend of a currency pair, but given the characteristics of the Forex market namely its chaotic, noisy and not stationary nature, prediction becomes a big challenge for traders when it comes to predicting accuracy. This paper aims to predict the right action to be taken at a certain moment through the development of a model that combines multiple techniques such multiple regression, simulated annealing meta-heuristics, reinforcement learning and technical indicators.

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“…Nam et al [10] carried out on furcating stock prices by utilizing big data. Fattah et al [11] predicts stock price trends using auto-machine learning.…”

Section: Introductionmentioning

confidence: 99%

Feature selection using non-parametric correlations and important features on recursive feature elimination for stock price prediction

Priyatno,

Ramadhan Sudirman,

Musridho

2024

IJECE

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Stock price prediction using machine learning is a rapidly growing area of research. However, the large number of features that can be used can complicate the learning process. The feature selection method that can be used to overcome this problem is recursive feature elimination. Standard recursive feature elimination carries the risk of producing inaccurate algorithms because the top-ranked features are not necessarily the most important features. This research proposes a feature selection method that combines important features and nonparametric correlation in recursive feature elimination for stock price prediction. The data features used are technical indicators and stock price history. The recursive feature elimination method is modified with important features and nonparametric correlation features. The strategy for combining important features and non-parametric features is average weight, 25:75% weight, 75:25% weight, maximum weight, and minimum weight. The performance evaluation results show that the proposed feature selection method succeeded in obtaining small error values. The proposed method for predicting PT Bank Rakyat Indonesia Tbk (BBRI) stock prices obtains mean squared error, root mean square error, mean absolute error, and mean absolute percentage error evaluation values of 0.0000336, 0.00577, 0.00459, and 1.78%, respectively. This shows that recursive feature elimination with feature selection that combines important features and non-parametric correlation works better than the original recursive feature elimination at predicting stock prices.

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AutoKeras and particle swarm optimization to predict the price trend of stock exchange

Cited by 4 publications

References 0 publications

An efficiency metaheuristic model to predicting customers churn in the business market with machine learning-based

An efficiency metaheuristic model to predicting customers churn in the business market with machine learning-based

Hybrid Forex prediction model using multiple regression, simulated annealing, reinforcement learning and technical analysis

Feature selection using non-parametric correlations and important features on recursive feature elimination for stock price prediction

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