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163 results for “Forecasting”
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Research ArticleOpen accessEvery word in the titleA Sentiment Analysis Approach for Affective State Recognition With SWISH-Activated DenseNet-169 in Human Event ForecastingKarthik Elangovan, Prabhakaran Paulraj, Dinesh Babu G L et al.Detecting human feelings from images is perhaps one of the most useful and challenging social communication problems in research. Emotion detection-based deep computing outperforms classic image processing techniques. Facial recognition technology has permeated every aspect of our daily lives, from unlocking our phones to getting private file access on our systems. Machine Learning and Deep Learning make it easy to … …Journal of Computer Science, Vol. 22, Issue 8 · 29 Jul 2026 · 1,211 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleAdaptive Cross-Validation Under Concept Drift for Time Series ForecastingKunjira Kingphai, Prapakorn Kanjina, Kamol Sanittham, Wacharong WongsanurakTime-series forecasting often involves non-stationary data, making i.i.d. validation unreliable and fixed-window protocols vulnerable to leakage and biased error estimates. We propose Adaptive Time-Series Cross-Validation (ATSCV), a drift-aware evaluation framework that uses statistical change-point detection to partition each series into contiguous, approximately stationary regimes, followed by forward-chaining folds that respect those boundaries. By aligning train–validation splits with distributional changes (with … …Journal of Computer Science, Vol. 22, Issue 7 · 7 Jul 2026 · 1,488 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleDeep 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 PDFCite
Research ArticleOpen accessEvery word in the titleStock Closing Price Forecasting Using LSTM, Sentiment Analysis, Kalman FilterAssel Abdildayeva, Galymzhan Nurtugan, Guldana Taganova, Ardak AkhmetovaThe article explores the problem of predicting the closing prices of stocks using an innovative approach that combines methods of machine learning, sentiment analysis and noise filtering. The focus is on the development and testing of a comprehensive model that integrates recurrent neural networks (LSTM) for time series analysis, the GPT-3 language model for processing and analyzing textual data from … …Journal of Computer Science, Vol. 20, Issue 11 · 4 Sep 2024 · 6,262 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleFlood Forecasting in Malawi's Shire River BasinDaniel Nakapu Hussein, Mwakio Tole, Maarifa MwakumanyaThis study describes a study conducted in 2021-2022 in the Shire River Basin (SRB) of Chikwawa District, Malawi to evaluate how flood forecasting is currently performed and to provide recommendations for improvement. Flooding often occurs in the basin, endangering people's lives and property. A case in point is the 2015 flood and 2019 Tropical Cyclone Idai, which were so devastating … …American Journal of Environmental Sciences, Vol. 19, Issue 2 · 30 May 2023 · 7,004 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleForecasting Ozone Concentrations Using Box-Jenkins ARIMA Modeling in MalaysiaWan Rozita Wan Mahiyuddin, Nur Izzah Jamil, Zamtira Seman et al.Time series analysis and forecasting has become a major tool in many applications in air pollution and environmental management fields. Among the most effective approaches for analyzing time series data is the model introduced by Box and Jenkins. In this study, we used Box-Jenkins methodology to build Autoregressive Integrated Moving Average (ARIMA) model on the average of monthly ozone data … …American Journal of Environmental Sciences, Vol. 14, Issue 3 · 25 May 2018 · 8,943 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleA VAR Model for Forecasting Land Market Value in USALei WangForecasts of the future tendency of economic variables such as GDP, inflation rate and unemployment rate, arise many interests from business and government. Also, Modeling the land market at the national level can capture rich dynamic presenting in interdependent economies. In this paper, we studied a Vector Auto-regression (VAR) of Land Market Value and five US macroeconomic variables. We employed … …Journal of Mathematics and Statistics, Vol. 14, Issue 1 · 1 Jan 2018 · 7,639 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleForecasting Bangladesh's Inflation through Econometric ModelsNazmul IslamThis research tries to sketch the concrete steps that help carry out to use ARIMA time series models for forecasting Bangladesh’s inflation. The focus, in this paper, is short-term basis annual inflation forecasting. For this purpose, different ARIMA models are used and the candid model is proposed. Based on the diagnostic and evaluation criteria, the most accurate model is selected. … …American Journal of Economics and Business Administration, Vol. 9, Issue 3 · 8 Nov 2017 · 9,195 accessesView PDFCite
Case ReportOpen accessEvery word in the titleEvaluation of Seasonal Autoregressive Integrated Moving Average Models for River Flow ForecastingKassahun Birhanu Tadesse, Megersa Olumana Dinka, Tena Alamirew, Semu Ayalew MogesReservoir operation policies cannot be functional in instant decision making without forecasting the future reservoir inflows. For forecasting inflows into reservoirs with only hydrological data is available like Koga irrigation dam, multivariate forecasting models cannot be used to generate accurate river flow information. As a result, an evaluation of univariate Seasonal Autoregressive Integrated Moving Average (SARIMA) models was done for … …American Journal of Environmental Sciences, Vol. 13, Issue 5 · 28 Sep 2017 · 7,885 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleParameters Estimate of Autoregressive Moving Average and Autoregressive Integrated Moving Average Models and Compare Their Ability for Inflow ForecastingMohammad Valipour, Mohammad Ebrahim Banihabib, Seyyed Mahmood Reza BehbahaniIn this study the ability of Autoregressive Moving Average (ARMA) and Autoregressive Integrated Moving Average (ARIMA) models in forecasting the monthly inflow of Dez dam reservoir located in Teleh Zang station in Dez dam upstream is estimated. ARIMA model has found a widespread application in many practical sciences. In addition, dam reservoir inflow forecasting is done by some methods such … …Journal of Mathematics and Statistics, Vol. 8, Issue 3 · 30 Aug 2012 · 17,288 accessesView PDFCite