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International Journal Artificial Intelligent and Informatics
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 5 Documents
 1 
Search results for , issue "Vol 2, No 2 (2024)" : 5 Documents clear
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)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (825.542 KB) | DOI: 10.33292/ijarlit.v2i2.36

Abstract

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)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (639.699 KB) | DOI: 10.33292/ijarlit.v2i2.39

Abstract

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.
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)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (356.383 KB) | DOI: 10.33292/ijarlit.v2i2.37

Abstract

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.
Stock Price Prediction of ReconAfrica (RECAF) Using Gated Recurrent Unit (GRU): Analysis and Implications for Investment Decisions Aníbal, Tomás López; Okanlawon, Rabiu
International Journal Artificial Intelligent and Informatics Vol 2, No 2 (2024)
Publisher : Research and Social Study Institute (ReSSI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33292/ijarlit.v2i2.35

Abstract

This study develops a stock price prediction model for ReconAfrica (RECAF) using Gated Recurrent Unit (GRU), an effective deep learning method for capturing temporal and non-linear patterns in stock price data. The model was trained and tested using five years of historical RECAF stock price data and evaluated with metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). The evaluation results show that the GRU model achieved an MAE of 0.0992, MSE of 0.0397, RMSE of 0.1993, and MAPE of 4.27, indicating a high predictive capability. These findings underscore the potential of the GRU model as a valuable tool for investors and market analysts in making more informed investment decisions. While the results are promising, the study also identifies opportunities for further development through the integration of external data and exploration of other deep learning architectures. Thus, this research contributes significantly to stock market analysis and improved investment strategies.
Forecasting Stock Prices of Taiwan Semiconductor Manufacturing Company (TSMC) Using Recurrent Neural Networks: Evaluating Predictive Performance in a Volatile Market li-We, Lai; Moanu, Koundé
International Journal Artificial Intelligent and Informatics Vol 2, No 2 (2024)
Publisher : Research and Social Study Institute (ReSSI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33292/ijarlit.v2i2.38

Abstract

Accurate stock price prediction plays a critical role in guiding investment strategies, particularly in dynamic industries such as semiconductors, where price volatility is high. This study investigates the effectiveness of Recurrent Neural Networks (RNN) in predicting the stock prices of Taiwan Semiconductor Manufacturing Company (TSMC), a global leader in the semiconductor sector. Using daily closing price data from January 2020 to January 2023, the RNN model was developed and trained to forecast future stock prices. The data was preprocessed with feature scaling to ensure the stability of model training, and a sliding window approach was applied to capture temporal dependencies. The model's predictive performance was evaluated using Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE) as key metrics. The RNN achieved an RMSE of 9.87 and a MAPE of 5.90%, indicating that the model provides reasonable accuracy in forecasting stock prices with a moderate level of deviation from actual values. Visual analysis further demonstrated the model's capacity to capture general trends in the stock price movements, although challenges were noted in predicting highly volatile periods. The study highlights the potential of RNN in financial forecasting while suggesting future improvements, such as incorporating advanced models like Long Short-Term Memory (LSTM) or external factors to enhance predictions during market volatility. These findings offer valuable insights for investors and analysts seeking to leverage machine learning in stock price prediction, particularly in industries characterized by rapid technological advancements and price fluctuations.

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