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163 results for “Forecasting”
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Research ArticleOpen accessEvery word in the titleDynamic Knowledge Driven Multi-Scale Spatiotemporal Fusion Framework for Reliable Multi-Horizon Traffic Flow ForecastingDeepika M S, P. Deepa Shenoy, Venugopal K RAccurate traffic flow forecasting remains a core challenge in intelligent transportation systems. Urban traffic patterns are characterised by complex spatiotemporal dependencies and inherent uncertainties, which complicate traffic forecasting. This study proposes a Dynamic Knowledge-Driven Spatiotemporal Fusion (DKDMSF) architecture to extract features of Multi-Scale traffic dynamics across various temporal traffic horizons. DKDMSF integrates Dynamic Spatial Attention (DSA) and Adaptive Graph Learning … …Journal of Computer Science, Vol. 22, Issue 9 · 30 Sep 2026 · 413 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleEfficient 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 PDFCite
Research ArticleOpen accessEvery word in the titleSales 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 PDFCite
Research ArticleOpen accessEvery word in the titleForecasting Model of Corn Commodity Production in Indonesia: Production and Operation Management-Quantitative Method (POM-QM) SoftwareAsriani, Dhian Herdhiansyah, Wa Embe, LM Fid AksaraFood is essential for humans. In addition to being consumed, it can also be a valuable commodity for economic purposes through the production of food crops. Therefore, this study aims to model the forecasting of maize production in Indonesia using Production and Operations Management-Quantitative Method (POM-QM) software. The uniqueness of this research lies in its innovative approach to predicting corn … …Journal of Computer Science, Vol. 20, Issue 4 · 20 Feb 2024 · 6,150 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleFinancial Forecasting with Machine Learning: Price Vs ReturnFiruz Kamalov, Ikhlaas Gurrib, Khairan RajabForecasting directional movement of stock price using machine learning tools has attracted a considerable amount of research. Two of the most common input features in a directional forecasting model are stock price and return. The choice between the former and the latter variables is often subjective. In this study, we compare the effectiveness of stock price and return as input … …Journal of Computer Science, Vol. 17, Issue 3 · 18 Mar 2021 · 8,430 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleA Time Series Forecasting for the Cumulative Confirmed and Critical Cases of the Covid-19 Pandemic in Saudi Arabia using Autoregressive Integrated Moving Average (ARIMA) ModelSamer H. Atawneh, Osamah A.M. Ghaleb, Ahmad MohdAziz Hussein et al.Reviews at present, different machine learning techniques and algorithms have been applied for predicting significant factors of the Coronavirus Disease-2019 (COVID-19) such as the outbreak and diagnosis. In this study, the most accurate time series forecasting model, namely, the Autoregressive Integrated Moving Average (ARIMA) model is used to forecast the expected cumulative number of confirmed and critical cases in Saudi … …Journal of Computer Science, Vol. 16, Issue 9 · 30 Sep 2020 · 7,596 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleForecasting Air Passenger Demand: A Comparison of LSTM and SARIMAQuang Hung Do, Shih-Kuei Lo, Jeng-Fung Chen et al.All airports need to have an accurate prediction of the number of passengers for their efficient management. An accurate prediction of the number of air passengers is crucial task since it provides information for planning decisions in the airport infrastructure to stabilize the service and maximize the profit. This study proposes a novel air passenger demand forecasting model based on … …Journal of Computer Science, Vol. 16, Issue 7 · 25 Jul 2020 · 8,555 accessesView PDFCite
Review ArticleOpen accessEvery word in the titleArtificial Intelligence Techniques and External Factors used in Crime Forecasting in Violence and Property: A ReviewRebaz Mala Nabi, Soran Ab. M. Saeed, Habibollah HaronCrime forecasting is beneficial in providing useful information to authorities in planning effective crime prevention measures. The two types of analysis used in crime forecasting are univariate and multivariate. Comparatively, multivariate analysis provides better forecasting accuracy because of its ability to discover crime patterns not previously seen. Crime is strongly influenced by several external factors, including economic, social and demographic. … …Journal of Computer Science, Vol. 16, Issue 2 · 19 Feb 2020 · 7,918 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleParameter Optimization of Gradient Tree Boosting Using Dragonfly Algorithm in Crime Forecasting and AnalysisAlif Ridzuan Khairuddin, Nor Azizah Ali, Razana Alwee et al.Crime forecasting and analysis are very important in predicting future crime patterns and beneficial to the authorities in planning effective crime prevention measures. One of the challenges found in crime analysis is the crime data itself as its form, representation and distribution are varied and unpredictable. To handle such data, most researchers have been focusing on applying various Artificial Intelligence … …Journal of Computer Science, Vol. 15, Issue 8 · 7 Aug 2019 · 7,918 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleAdvanced Multivariate Time Series Forecasting ModelsMiss Lei WangIn this study, the focus is to collect and summarize various advanced forecasting models for multivariate time series dataset. We have discussed about the inherent forecasting strengths and weaknesses related to these time series modelings. Also, the main section deal with the experience of using such data in econometric analysis. Besides, the implementation of SAS and R softwares improve the … …Journal of Mathematics and Statistics, Vol. 14, Issue 1 · 21 Dec 2018 · 7,316 accessesView PDFCite