22 results for “Short Term Forecasting”

Set a search alert

Research ArticleOpen accessEvery word in the titleWeather Forecasting Using Merged Long Short-Term Memory Model (LSTM) and Autoregressive Integrated Moving Average (ARIMA) ModelAfan Galih Salman, Yaya Heryadi, Edi Abdurahman, Wayan SupartaWeather forecasting is an interesting research problem in flight navigation area. One of the important weather data in aviation is visibility. Visibility is an important factor in all phases of flight, especially when the aircraft is maneuvering on or close to the ground, i.e., during taxi-out, take-off and initial climb, approach and landing and taxi-in. The aim of these study … …Journal of Computer Science, Vol. 14, Issue 7 · 28 Jun 2018 · 11,422 accessesView PDF Cite

Research ArticleOpen accessEvery word in the titleShort-Term and Medium-Term Load Forecasting for Jordan's Power SystemI. Badran, H. El-Zayyat, G. HalasaSeveral electric power companies are now forecasting electric loads based on conventional methods. However, since the relationship between loads and factors influencing these loads is nonlinear, it is difficult to identify its nonlinearity by using conventional methods. Most of papers deal with 24-h-ahead load forecasting or next day peak load forecasting. These methods forecast the demand power by using forecasted … …American Journal of Applied Sciences, Vol. 5, Issue 7 · 31 Jul 2008 · 7,081 accessesView PDF Cite

Research ArticleOpen accessEvery word in the titleEnhancing Dam Safety and Management: Long Short-Term Memory Based Predictive Models for Accurate Alert ForecastingNisha C. M, N. ThangarasuDam management and early alert systems are critical for effective water resource management. Accurate prediction of dam alert signals facilitates proactive decision-making, thereby aiding in the effective management and reduction of potential risks linked to dam operations. Within this research, Long Short-Term Memory (LSTM) networks are utilized to forecast dam alert signals issued from the dam by leveraging daily parameters, … …Journal of Computer Science, Vol. 22, Issue 1 · 6 Feb 2026 · 1,626 accessesView PDF Cite

Research ArticleOpen accessIn the keywordsElectrical Load Forecasting Using Artificial Neural Network: The Case Study of the Grid Inter-Connected Network of Benin Electricity Community (CEB)Adekunlé Akim Salami, Ayité Sénah Akoda Ajavon, Koffi A. Dotche, Koffi-Sa BedjaThe low rate of electrification seems challenging in many West African countries and many strategies are underway to improve upon. In this regard, the target of achieving the universal access and services calls for a stable and reliable electrical network. Forecasting of electrical load on a connected grid network is very delicate and requires tremendous task from the utilities (billing … …American Journal of Engineering and Applied Sciences, Vol. 11, Issue 2 · 11 Apr 2018 · 9,143 accessesView PDF Cite

Research ArticleOpen accessIn the abstractDeep Learning Models for Predicting Stock Closing Prices in the Saudi Stock MarketAreej Alhumaidi, Hoda A. AbdelhafezFinancial time series data are inherently volatile and nonlinear, presenting considerable challenges in accurately forecasting stock market trends. This study aims to predict the closing prices of companies listed on the Saudi stock market by analyzing historical data spanning from 2013 to 2023. To this end, four deep learning models were employed-Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), Gated Recurrent … …Journal of Computer Science, Vol. 22, Issue 4 · 22 Apr 2026 · 1,355 accessesView PDF Cite

Research ArticleOpen accessIn the abstractEfficient Rainfall Forecasting Using Sequential Momentum-Based Artificial Neural Networks for Urban Flood ManagementSherin K K, G. SuganthiUrban floods cause severe disruption in densely populated cities and damage essential public infrastructure. Rapid urbanization and weak drainage systems further increase the risk during heavy rainfall. Accurate short-term rainfall forecasting is required to support early warning and reduce losses. This research focuses on improving rainfall prediction accuracy using an Artificial Neural Network trained with a new optimizer named Sequential … …Journal of Computer Science, Vol. 22, Issue 4 · 9 Apr 2026 · 1,539 accessesView PDF Cite

Research ArticleOpen accessIn the abstractA Dynamic Lifelong Learning Network for Real-Time Stock Prediction With Reinforcement TuningTalent Mawere, Selvaraj Rajalakshmi, Venu Madhav Kuthadi, Otlhapile DinakanyaneCompanies and investors require accurate market forecasting to make more informed decisions. Traditional methods for predicting stock market performance have become less useful considering the dynamic and volatile nature that characterizes stock markets today. In this paper, we propose an innovative Dynamic Lifelong Learning Network for stock market prediction. The model incorporates a hybrid convolutional long short-term memory with an … …Journal of Computer Science, Vol. 22, Issue 2 · 20 Feb 2026 · 1,822 accessesView PDF Cite

Research ArticleOpen accessIn the abstractPredictive Analytics and Procurement Visibility: A Big-Data-Driven Approach to Customer Satisfaction in Supply Chain ManagementDevendra Nath Pathak, Rakesh Kumar Yadav, Hitendra SinghIn the current dynamic and consumer driven market environment, supply chains need to move out of reactive to the proactive ecosystems. One of the most urgent requirements is increasing procurement visibility and the development of better order planning for the inventory to meet the products’ demand with continued high customer satisfaction. This paper is an agenda of big-data-driven predictive analytics … …Journal of Computer Science, Vol. 22, Issue 1 · 3 Feb 2026 · 2,442 accessesView PDF Cite

Research ArticleOpen accessIn the abstractDeep Learning Synergy: CNNs and Transformers for Epidemic Outbreak ForecastingAlexander R., Shakthi Priya V., Sumathi G. et al.An Accurate prediction of outbreaks is extremely crucial for taking proactive public health interventions, distributing limited resources, and controlling a disease. This paper assesses and also compares the performances of the available deep learning models, namely, Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) networks, and Bidirectional Long Short-Term Memory (BiLSTM) networks, in predicting. CNNs are best for feature extraction … …Journal of Computer Science, Vol. 21, Issue 12 · 23 Jan 2026 · 2,113 accessesView PDF Cite

Research ArticleOpen accessIn the abstractSales Forecasting Models: Comparison between ARIMA, LSTM and ProphetDmitry BrykinSales forecasting is crucial for business planning and resource allocation. Data-driven approaches have become popular in this field. This study compares the performance of three forecasting models: Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM) and Prophet within the context of specific sales categories derived from acquiring data provided by a bank. This study uses a time series dataset … …Journal of Computer Science, Vol. 20, Issue 10 · 1 Aug 2024 · 7,981 accessesView PDF Cite