Tversky-RideNN based Feature Fusion and Optimized Deep RNN for Stock Market Prediction

Vanguri, Nagarjun Yadav; Pazhanirajan, S.; Kumar, T. Anil

doi:10.1109/icirca54612.2022.9985572

Cited by 2 publications

(1 citation statement)

References 30 publications

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“…Deep learning models could increase the precision of stock price predictions even further compared to conventional machine learning models. Zaheer [7] et al proposed a deep hybrid model CNN-LSTM-RNN and compared it with RNN [8], LSTM [9], CNN-LSTM [10], and so on, and the results of the experiments proved that the presented model is superior to other models. Chen [11] et al proposed a share forecasting model with a gated recursive unit (GRU) and reorganized dataset to solve the overfitting problem and improve the forecasting accuracy.…”

Section: Introductionmentioning

confidence: 99%

An Optimized Hybrid Deep Learning Model with Dung Beetle Optimizer for Stock Price Prediction

2023

Proceedings of the 2nd International Conference on Public Management, Digital Economy and Internet Technology, ICPDI 2023, Sept

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Stock prices are known to vary nonlinearly, which makes stock price forecasting quite difficult. Therefore linear models cannot accurately predict frequently fluctuating stock prices; instead, nonlinear models such as gated recurrent unit (GRU) and temporal convolutional network (TCN) tend to outperform linear models in stock price prediction. And yet, nonlinear models that are not well optimized to forecast unstable stock data generally result in poor fit and instability problems. To improve the fitting and stability of a model for share price forecasting, it is essential to optimize the parameters of a model. Dung beetle optimizer (DBO) is a novel bee colony intelligent optimization algorithm that can be used to optimize nonlinear models to enhance the precision of stock price forecasting. In the paper, we first present the GRU-TCN hybrid model with the dung beetle optimization algorithm for stock price prediction, which alleviates the poor fitting and instability problems of stock price prediction to a certain extent and improves the accuracy of stock price prediction. We conducted extensive experiments to show that GRU-TCN-DBO has better performance on MSE and 𝑹 𝟐 evaluation metrics compared to GRU-TCN, GRU, TCN, and LSTM by using 30 stocks of the Dow Jones Industrial Average.

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Section: Introductionmentioning

confidence: 99%

An Optimized Hybrid Deep Learning Model with Dung Beetle Optimizer for Stock Price Prediction

2023

Proceedings of the 2nd International Conference on Public Management, Digital Economy and Internet Technology, ICPDI 2023, Sept

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show abstract

A Self-Attention-Based Stock Prediction Method Using Long Short-Term Memory Network Architecture

Ye,

Ning,

Bian

et al. 2023

Communications in Computer and Information Science

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Tversky-RideNN based Feature Fusion and Optimized Deep RNN for Stock Market Prediction

Cited by 2 publications

References 30 publications

An Optimized Hybrid Deep Learning Model with Dung Beetle Optimizer for Stock Price Prediction

An Optimized Hybrid Deep Learning Model with Dung Beetle Optimizer for Stock Price Prediction

A Self-Attention-Based Stock Prediction Method Using Long Short-Term Memory Network Architecture

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