The two-stage machine learning ensemble models for stock price prediction by combining mode decomposition, extreme learning machine and improved harmony search algorithm

Jiang, Manrui; Jia, Lifen; Chen, Zhen‐Song; Chen, Wei

doi:10.1007/s10479-020-03690-w

Cited by 53 publications

(12 citation statements)

References 66 publications

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“…Statistical models, machine learning, and deep learning models have been used in the literature to predict financial asset prices (Abedin et al, 2020;Akyildirim et al, 2021;Cui et al, 2020;Fischer & Krauss, 2018;Guotai et al, 2017;Jiang et al, 2020;Kyriakou et al, 2021;Shajalal et al, 2021;Xia et al, 2020). We apply machine learning and deep learning algorithms to measure different types of errors and find the best model for the dataset to measure the prediction accuracy for each currency against USD.…”

Section: Methodsmentioning

confidence: 99%

Deep learning-based exchange rate prediction during the COVID-19 pandemic

et al. 2021

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This study proposes an ensemble deep learning approach that integrates Bagging Ridge (BR) regression with Bi-directional Long Short-Term Memory (Bi-LSTM) neural networks used as base regressors to become a Bi-LSTM BR approach. Bi-LSTM BR was used to predict the exchange rates of 21 currencies against the USD during the pre-COVID-19 and COVID-19 periods. To demonstrate the effectiveness of our proposed model, we compared the prediction performance with several more traditional machine learning algorithms, such as the regression tree, support vector regression, and random forest regression, and deep learning-based algorithms such as LSTM and Bi-LSTM. Our proposed ensemble deep learning approach outperformed the compared models in forecasting exchange rates in terms of prediction error. However, the performance of the model significantly varied during non-COVID-19 and COVID-19 periods across currencies, indicating the essential role of prediction models in periods of highly volatile foreign currency markets. By providing an improved prediction performance and identifying the most seriously affected currencies, this study is beneficial for foreign exchange traders and other stakeholders in that it offers opportunities for potential trading profitability and for reducing the impact of increased currency risk during the pandemic.

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Section: Methodsmentioning

confidence: 99%

Deep learning-based exchange rate prediction during the COVID-19 pandemic

et al. 2021

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“…Another limitation is the use of single models for developing the fraud detection framework in this study. To further enhance the developed framework, hybrid models can be formed using combination of two or more models (Jiang et al, 2020 ). Hybrid models enable the use of more than one model to determine the transaction legitimacy, in order to improve further the fraud detection rate.…”

Section: Limitations and Further Researchmentioning

confidence: 99%

An intelligent payment card fraud detection system

et al. 2021

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Payment cards offer a simple and convenient method for making purchases. Owing to the increase in the usage of payment cards, especially in online purchases, fraud cases are on the rise. The rise creates financial risk and uncertainty, as in the commercial sector, it incurs billions of losses each year. However, real transaction records that can facilitate the development of effective predictive models for fraud detection are difficult to obtain, mainly because of issues related to confidentially of customer information. In this paper, we apply a total of 13 statistical and machine learning models for payment card fraud detection using both publicly available and real transaction records. The results from both original features and aggregated features are analyzed and compared. A statistical hypothesis test is conducted to evaluate whether the aggregated features identified by a genetic algorithm can offer a better discriminative power, as compared with the original features, in fraud detection. The outcomes positively ascertain the effectiveness of using aggregated features for undertaking real-world payment card fraud detection problems.

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“…A hybrid model is a combination of several single models through an optimization algorithm. It can inherit the advantages of every single model, thereby improving the prediction accuracy and stability (Guotai et al, 2017b ; Hu et al, 2021 ; Jiang et al, 2020 ; Khalilpourazari and Doulabi, 2021 ). In existing studies, the most commonly used optimization algorithms are the genetic algorithm (GA) (Yang et al, 2019 ), particle swarm optimization (PSO) (Ribeiro et al, 2021 ), ant lion optimization algorithm (ALO) (P. et al, 2018 ), frog-leaping algorithm (FLA) (He et al, 2021 ), and whale optimization algorithm (WOA) (Lin and Zhang, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Point and interval prediction of crude oil futures prices based on chaos theory and multiobjective slime mold algorithm

et al. 2022

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Crude oil is the most important energy source in the world, and fluctuations in oil prices can significantly influence investors, companies, and governments. However, crude oil prices have numerous characteristics, including randomness, sudden structural changes, intrinsic nonlinearity, volatility, and chaotic nature. This makes the accurate forecasting of crude oil prices a difficult and challenging task. In this paper, a hybrid prediction model for crude oil futures prices is proposed, the accuracy and robustness of which are demonstrated via controlled experiments and sensitivity analysis. This study uses a new data denoising method for data processing to improve the accuracy and stability of the predictions of crude oil prices. Furthermore, the chaotic time-series prediction method, shallow neural networks, linear model prediction methods, and deep learning methods are adopted as submodels. The results of interval forecasts with narrow widths and high prediction accuracies are derived by introducing a confidence interval adjustment coefficient. The results of the simulation experiments indicate that the proposed hybrid prediction model exhibits higher accuracy and efficiency, as well as better robustness of the forecasting than the control models. In summary, the proposed forecasting framework can derive accurate point and interval forecasts and provide a valuable reference for the price forecasting of crude oil futures.

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The two-stage machine learning ensemble models for stock price prediction by combining mode decomposition, extreme learning machine and improved harmony search algorithm

Cited by 53 publications

References 66 publications

Deep learning-based exchange rate prediction during the COVID-19 pandemic

Deep learning-based exchange rate prediction during the COVID-19 pandemic

An intelligent payment card fraud detection system

Point and interval prediction of crude oil futures prices based on chaos theory and multiobjective slime mold algorithm

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