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International Journal Artificial Intelligent and Informatics

IJARLIT
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Published by Research and Social Study Institute

ISSN : - EISSN : 2622626X DOI : https://doi.org/10.33292

Core Subject : Economy, Science,

Computer Science & IT Economics, Econometrics & Finance

International Journal of Artificial Intelligence and Informatics is a scientific journal dedicated to the exploration of theories, methods, and applications of artificial intelligence in time series analysis, forecasting, and prediction. This journal serves as a platform for researchers, academics, and practitioners to publish their work on predictive models applied to various time-dependent phenomena. Topics within the journal’s scope include, but are not limited to: 1. Predictive Methodologies and Models Deep learning models for forecasting (LSTM, GRU, Transformer, etc.) Machine learning algorithms for time series forecasting (ARIMA, SARIMA, XGBoost, etc.) Optimization of forecasting models using metaheuristic approaches (PSO, GA, etc.) Hybrid models for improving prediction accuracy Statistical methods and Bayesian approaches in forecasting 2. Applications of Time Series and Forecasting Across Various Fields Financial and stock market prediction Weather forecasting and climate change analysis Energy demand forecasting and resource management Time series analysis in healthcare and epidemiology Forecasting in manufacturing and supply chain management User behavior prediction in e-commerce and social media 3. Data and Infrastructure for Forecasting Big data management in time series analysis Streaming data and real-time forecasting Explainable AI (XAI) in predictive models Data augmentation and synthetic data for forecasting The journal welcomes research articles, review papers, and case studies that provide significant contributions to the development of theories and implementation of predictive systems based on artificial intelligence.

Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan

Articles 30 Documents

1 2 3

Comparative Analysis of LSTM and Grid Search Optimized LSTM for Stock Prediction: Case Study of Africa Energy Corp. (AFE.V) Mokona, Boho; Shipo, Ngezana
International Journal Artificial Intelligent and Informatics Vol 2, No 1 (2024)
Publisher : Research and Social Study Institute (ReSSI)

This research examines the effectiveness of Long Short-Term Memory (LSTM) neural networks for predicting Africa Energy Corp. (AFE.V) stock prices, comparing a standard LSTM implementation with a Grid Search optimized LSTM model. The research shows that hyperparameter optimization through Grid Search significantly improves prediction accuracy. The optimized LSTM model achieved superior performance across all evaluation metrics, with a test RMSE of 0.01, MAE of 0.01, MAPE of 3.41%, and R² of 0.9518, showing substantial improvement over the model without optimization. These findings emphasize the importance of hyperparameter tuning in deep learning models for financial time series forecasting and provide empirical evidence supporting the application of optimized LSTM networks for stock price prediction.

Comparison of GRU and CNN Methods for Predicting the Exchange Rate of Argentine Peso (ARS) against US Dollar (USD) Agustin, Facundo; Melin, Patricia De
International Journal Artificial Intelligent and Informatics Vol 2, No 1 (2024)
Publisher : Research and Social Study Institute (ReSSI)

This study aims to compare the performance of the Gated Recurrent Unit (GRU) and Convolutional Neural Network (CNN) methods in predicting the exchange rate of the Argentine Peso (ARS) against the United States Dollar (USD). Using historical exchange rate data from January 2017 to December 2022, both models were trained and evaluated using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R² Score metrics. The results showed that the GRU model outperformed the CNN model in all evaluation metrics with MSE of 1.907 compared to 3.273 for CNN, RMSE of 1.381 compared to 1.809 for CNN, MAE of 1.063 compared to 1.433 for CNN, and R² Score of 0.996 compared to 0.994 for CNN. This study shows that GRU is more effective in capturing temporal patterns in currency exchange rate data compared to CNN, which highlights the advantages of recurrent architecture for financial time series prediction problems.

Performance Comparison of GRU and LSTM Methods for Predicting Bitcoin Exchange Rate against US Dollar Kepo, Ronal; Okokpujie, Daniel
International Journal Artificial Intelligent and Informatics Vol 2, No 1 (2024)
Publisher : Research and Social Study Institute (ReSSI)

This research aims to compare the performance between the Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) methods in predicting the Bitcoin exchange rate against the US Dollar (BTC-USD). The data used comes from Yahoo Finance for the period 2017-2022. Each model is built with a comparable architecture and evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), coefficient of determination (R²), and prediction accuracy metrics. The results show that the LSTM model performed better on the test data with a MAPE of 3.80% and an accuracy of 96.20%, while the GRU model achieved a MAPE of 5.13% and an accuracy of 94.87%. Although the GRU model performed better on the training data, the LSTM model showed better generalization ability on the testing data. This research provides important insights into the selection of the optimal recurrent neural network architecture for Bitcoin exchange rate prediction which is known for its high volatility.

Prediction of the Exchange Rate of the Russian Ruble (RUB) against the United States Dollar (USD): Performance Comparison of LSTM and CNN Models Dzhumashev, Dzhumashev; Aybek, Maksat
International Journal Artificial Intelligent and Informatics Vol 2, No 1 (2024)
Publisher : Research and Social Study Institute (ReSSI)

This research aims to compare the effectiveness of Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) models in predicting the exchange rate of the Russian Rouble (RUB) against the United States Dollar (USD). Currency exchange rates have complex time series characteristics with high volatility, especially for an economy like Russia that is affected by various geopolitical and economic factors. Both models were trained using historical USDRUB exchange rate data and evaluated based on Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), coefficient of determination (R²), and prediction accuracy metrics. The results showed that the LSTM model outperformed CNN on all evaluation metrics with RMSE of 4.42 (versus 4.99 for CNN), MAE of 1.67 (versus 2.00 for CNN), MAPE of 1.76% (versus 2.12% for CNN), and R² of 0.8775 (versus 0.8079 for CNN) on the test data. These findings indicate that the LSTM's ability to model long-term dependencies provides a significant advantage in predicting currency exchange rates compared to convolution-based approaches. This research provides important insights for monetary policy makers, financial market analysts, and international business people who depend on accurate exchange rate predictions for strategic decision making.

Stock Price Prediction of Thai Oil Public Company Limited (TOP.BK) Using LSTM Model with Grid Search Hyperparameter optimization Sanhatham, Sutthipong
International Journal Artificial Intelligent and Informatics Vol 2, No 1 (2024)
Publisher : Research and Social Study Institute (ReSSI)

This study examines the effectiveness of the Long Short-Term Memory (LSTM) model in predicting stock price movements of Thai Oil Public Company Limited (TOP.BK). Using historical stock price data of the past five years, we apply the LSTM neural network architecture to model temporal patterns and predict future stock prices. The model is compared with traditional time series approaches such as ARIMA and other statistical models. Results show that the LSTM model optimized by Grid Search achieves excellent performance with Root Mean Square Error (RMSE) of 1.00 and Mean Absolute Error (MAE) of 0.75 on the test data, with prediction accuracy reaching 98.32%. The model also showed a high coefficient of determination (R²) of 0.8715 on the test data, demonstrating the model's ability to explain most of the variation in the data. This research proves that the LSTM model is highly effective for stock price prediction in the oil and gas industry, with important implications for investment strategies and risk management.

Enhancing Stock Price Predictions: Leveraging LSTM for Accurate Forecasting of Ecopetrol's Stock Performance Muñoz, Jaminton; Castaño, K
International Journal Artificial Intelligent and Informatics Vol 2, No 2 (2024)
Publisher : Research and Social Study Institute (ReSSI)

Accurate stock price prediction remains a significant challenge in financial forecasting, particularly for emerging market stocks. This study investigates the efficacy of Long Short-Term Memory (LSTM) networks in forecasting the stock prices of Ecopetrol (EC), Colombia's largest oil and gas company. Using historical stock data from Yahoo Finance spanning September 18, 2018, to September 18, 2023, we developed an LSTM model to capture complex temporal patterns in the stock market. The model's performance was evaluated using a range of metrics including Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and Mean Absolute Scaled Error (MASE). The results demonstrate that the LSTM model achieves low MAE (0.2509652), MSE (0.11678666), and RMSE (0.34174064), alongside a MAPE of 2.071206, indicating high accuracy and reliability in predicting stock prices. Although the MASE of 1.125679 suggests that the model performs similarly to a naive forecasting approach, it still provides valuable insights into stock price movements. This study highlights the effectiveness of LSTM in handling sequential data and capturing intricate stock price patterns, while suggesting that future improvements could be made by optimizing the model further and integrating additional relevant features.

Harnessing Convolutional Neural Networks for Accurate Stock Price Prediction: A Case Study of Hellenic Telecommunications Organization (HTO.AT) Tzoulis, Giannis
International Journal Artificial Intelligent and Informatics Vol 2, No 2 (2024)
Publisher : Research and Social Study Institute (ReSSI)

This study presents a novel approach to stock price prediction by employing Convolutional Neural Networks (CNNs) to forecast the stock prices of the Hellenic Telecommunications Organization (HTO.AT). The CNN model demonstrated exceptional predictive performance, achieving a Root Mean Squared Error (RMSE) of 0.22859211 and a Mean Absolute Percentage Error (MAPE) of 1.2041852, indicating a high level of accuracy. By effectively capturing complex and non-linear patterns in historical stock price data, the model surpasses traditional forecasting methods, thus offering significant advantages for investors and financial analysts. This research emphasizes the importance of integrating external data and exploring alternative deep learning architectures, such as Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) networks, to further enhance prediction capabilities. Overall, the findings underscore the potential of CNNs as powerful tools in financial market analysis, providing actionable insights for more informed investment decisions.

Performance Comparison of Long Short-Term Memory and Convolutional Neural Network for Prediction of Exchange Rate of Indian Rupee against US Dollar Rai, Kovat; Vijayan, Amit
International Journal Artificial Intelligent and Informatics Vol 3, No 1 (2025)
Publisher : Research and Social Study Institute (ReSSI)

This study compares the effectiveness of Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) models in predicting the exchange rate of the Indian Rupee (INR) against the United States Dollar (USD). Using historical data from 2017 to 2023 obtained from Yahoo Finance, both models were trained and evaluated based on several performance metrics including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), coefficient of determination (R²), and accuracy. The results showed that the hybrid LSTM model consistently outperformed the CNN model on all evaluation metrics, with a Test RMSE value of 0.38 compared to 1.32 for CNN. The LSTM model also showed better stability between training and testing performance, indicating better generalization ability and lower risk of overfitting. These findings confirm the superiority of the LSTM architecture in capturing the complex temporal patterns inherent in financial time series data, making it a more reliable option for currency exchange rate prediction.

Comparison of CNN-LSTM Hybrid and CNN Methods for Ethereum (ETH) to US Dollar (USD) Exchange Rate Prediction Regine, Daniel; Zabarnyi, Anatoly
International Journal Artificial Intelligent and Informatics Vol 3, No 1 (2025)
Publisher : Research and Social Study Institute (ReSSI)

This research compares the effectiveness of the hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) method and the Convolutional Neural Network (CNN) method in predicting the Ethereum (ETH) exchange rate against the United States Dollar (USD). The research uses historical ETH/USD data from Yahoo Finance for the period 2017-2022. Evaluation of the two models was carried out using the performance metrics Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), coefficient of determination (R²), and accuracy rate. The results showed that the CNN-LSTM hybrid model significantly outperformed the CNN model in predicting the ETH/USD exchange rate with a Test RMSE value of 94.67 compared to 129.02 for CNN, as well as an accuracy rate of 96.31% versus 94.89%. These findings contribute to the fintech literature by providing empirical evidence of the superiority of hybrid methods for high volatility cryptocurrency exchange rate prediction.

Optimizing Bidirectional LSTM for Energy Consumption Prediction Using Chaotic Particle Swarm Optimization and Hyperparameter Tuning Cahyo Kusuma, Candra Juni; Khairunnisa, Khairunnisa
International Journal Artificial Intelligent and Informatics Vol 2, No 2 (2024)
Publisher : Research and Social Study Institute (ReSSI)

This study aims to improve the accuracy of energy consumption prediction using the Bidirectional Long Short-Term Memory (BLSTM) model which is known to be able to handle temporal dependencies in time series data. However, the performance of BLSTM is greatly affected by the hyperparameter configuration, which often requires manual tuning which is inefficient. To address this, this study proposes an optimization framework that combines BLSTM with Chaotic Particle Swarm Optimization (CPSO) to automatically adjust hyperparameters such as the number of hidden units and learning rate. Experiments show that BLSTM optimized with CPSO produces higher prediction accuracy compared to traditional methods such as grid search and random search. By utilizing the chaos map, CPSO improves exploration and exploitation capabilities, accelerates convergence, and finds more optimal solutions. The integration of CPSO and BLSTM shows promising results for improving the performance of time series prediction models, especially in energy consumption forecasting.

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OAI Link

Editorial Team

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Contact Name Auralia

Contact Email submissions@ijarlit.org

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Journal Mail Official submissions@ijarlit.org

Editorial Address Prenggan, Kotagede, Kota Yogyakarta, Daerah Istimewa Yogyakarta 55172, Indonesia

Location Kota yogyakarta, Daerah istimewa yogyakarta INDONESIA

Abstract

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Contact Name
Auralia

Contact Email
submissions@ijarlit.org

Phone
-

Journal Mail Official
submissions@ijarlit.org

Editorial Address
Prenggan, Kotagede, Kota Yogyakarta, Daerah Istimewa Yogyakarta 55172, Indonesia

Location
Kota yogyakarta,

Daerah istimewa yogyakarta

INDONESIA