Data Driven Artificial Neural Network LSTM Hybrid Predictive Model Applied for International Stock Index Prediction

Safari, Ashkan

doi:10.1109/icwr54782.2022.9786223

Cited by 8 publications

(3 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore (Jamil et al, 2021), discusses the significance of AI in smart grids (Safari and Ghavifekra, 2021a). discusses quantum technologies in the context of smart grids, with a focus on predictive characteristics (Safari, 2022). uses precise AI-driven stock estimates (Safari and Ghavifekrb, 2021b).…”

Section: Literature Reviewmentioning

confidence: 99%

DeepResTrade: a peer-to-peer LSTM-decision tree-based price prediction and blockchain-enhanced trading system for renewable energy decentralized markets

Safari,

Gharehbagh,

Nazari-Heris

et al. 2023

Front. Energy Res.

View full text Add to dashboard Cite

Intelligent predictive models are fundamental in peer-to-peer (P2P) energy trading as they properly estimate supply and demand variations and optimize energy distribution, and the other featured values, for participants in decentralized energy marketplaces. Consequently, DeepResTrade is a research work that presents an advanced model for predicting prices in a given traditional energy market. This model includes numerous fundamental components, including the concept of P2P trading systems, long-term and short-term memory (LSTM) networks, decision trees (DT), and Blockchain. DeepResTrade utilized a dataset with 70,084 data points, which included maximum/minimum capacities, as well as renewable generation, and price utilized of the communities. The developed model obtains a significant predictive performance of 0.000636% Mean Absolute Percentage Error (MAPE) and 0.000975% Root Mean Square Percentage Error (RMSPE). DeepResTrade’s performance is demonstrated by its RMSE of 0.016079 and MAE of 0.009125, indicating its capacity to reduce the difference between anticipated and actual prices. The model performs admirably in describing actual price variations in, as shown by a considerable R2 score of 0.999998. Furthermore, F1/recall scores of [1, 1, 1] with a precision of 1, all imply its accuracy.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

DeepResTrade: a peer-to-peer LSTM-decision tree-based price prediction and blockchain-enhanced trading system for renewable energy decentralized markets

Safari,

Gharehbagh,

Nazari-Heris

et al. 2023

Front. Energy Res.

View full text Add to dashboard Cite

show abstract

“…Reference [36] presents quantum technologies in the context of smart grids, with a focus on predictive characteristics. Reference [37] uses precise AI-driven estimators on a dataset. Reference [38] provides dependable AI-powered output projections.…”

Section: Introductionmentioning

confidence: 99%

DeepVELOX: INVELOX Wind Turbine Intelligent Power Forecasting Using Hybrid GWO–GBR Algorithm

Safari,

Kheirandish Gharehbagh,

Nazari Heris

2023

Energies

Self Cite

View full text Add to dashboard Cite

The transition to sustainable electricity generation depends heavily on renewable energy sources, particularly wind power. Making precise forecasts, which calls for clever predictive controllers, is a crucial aspect of maximizing the efficiency of wind turbines. This study presents DeepVELOX, a new methodology. With this method, sophisticated machine learning methods are smoothly incorporated into wind power systems. The Increased Velocity (IN-VELOX) wind turbine framework combines the Gradient Boosting Regressor (GBR) with the Grey Wolf Optimization (GWO) algorithm. Predictive capabilities are entering a new age thanks to this integration. This research presents DeepVELOX, its structure, and results. In particular, this study presents the considerable performance of DeepVELOX. With a MAPE of 0.0002 and an RMSPE of 0.0974, it gets outstanding Key Performance Indicator (KPI) results. The criteria of Accuracy, F1-Score, R2-Score, Precision, and Recall, with a value of 1, further emphasize its performance. The result of this process is an MSE of 0.0352. The significant reduction in forecast disparities is made possible by this system’s remarkable accuracy. Along with improving accuracy, the integration of machine learning algorithms, including GBR, the GWO algorithm, and wind turbine operations, offer a dynamic framework for maximizing power and energy capture.

show abstract

“…[11] and Ref. [12] developed an attention‐based LSTM hybrid predictive model to obtain index forecasting, based on stock price data. The mentioned ALSTM modellings achieved the RMSPE of %4 and %8.49, respectively.…”

Section: Introductionmentioning

confidence: 99%

Stock index forecasting using DACLAMNN: A new intelligent highly accurate hybrid ACLSTM/Markov neural network predictor

Safari,

Badamchizadeh

2023

Cognitive Comp and Systems

Self Cite

View full text Add to dashboard Cite

The authors present the investigation of a new hybrid predictive model of Duplex Attention‐based Coupled LSTM Markov Averaged Neural Network, known as DACLMANN. The financial field, particularly the stock market, heavily relies on accurate predictive models. DACLMANN comprises four essential components: two LSTM blocks, an Averagiser and a Markov Neural Network block. The first LSTM block is composed of two hidden layers, each containing 50 neurons and a dense layer with 25 neurons. The second LSTM block consists of two hidden layers, each with 100 neurons, and a dense layer with 50 neurons. The Averagiser plays a crucial role by averaging the closing prices and predicted values from the first LSTM block, resulting in a 90% gain. These averaged values are then fed into the second LSTM block for further prediction. Finally, the predictions undergo evaluation using the Markov model, yielding the final prediction. To assess the performance of DACLMANN, it was tested on 22 years of stock prices for the AMZN index. The evaluation metrics used by the authors include an R2 of 0.76, mean absolute error of 6.81216, root mean square error of 8.6040, Precision of 1, Accuracy of 1, Recall of 1 and F1 of 1. Additionally, DACLMANN achieved a Mean Absolute Percentage Error of less than 0.043% and an RMSPE of less than 2.1%. These results not only demonstrate the effectiveness of the proposed model but also authenticate the prediction outcomes. DACLMANN offers several advantages over traditional predictive models in the stock market. By combining the strengths of Duplex Attention‐based Coupled LSTM, Averagiser, and Markov Neural Network, DACLMANN leverages the power of deep learning, attention mechanisms, and sequential modelling. This hybrid approach enables DACLMANN to capture intricate patterns and dependencies present in stock market data, leading to more accurate and reliable predictions. The robust evaluation metrics further validate the superiority of DACLMANN in predicting stock prices.

show abstract

Data Driven Artificial Neural Network LSTM Hybrid Predictive Model Applied for International Stock Index Prediction

Cited by 8 publications

References 13 publications

DeepResTrade: a peer-to-peer LSTM-decision tree-based price prediction and blockchain-enhanced trading system for renewable energy decentralized markets

DeepResTrade: a peer-to-peer LSTM-decision tree-based price prediction and blockchain-enhanced trading system for renewable energy decentralized markets

DeepVELOX: INVELOX Wind Turbine Intelligent Power Forecasting Using Hybrid GWO–GBR Algorithm

Stock index forecasting using DACLAMNN: A new intelligent highly accurate hybrid ACLSTM/Markov neural network predictor

Contact Info

Product

Resources

About