Consumers’ online shopping choices and assortment forecasting by ABC analysis using eye tracking technology by N.N. Kalkova

“…Using the case study of cheese, the paper aims to reveal peculiarities of forecasting consumers’ online shopping choices with the help of eye-tracking technology. …”
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On Local Antimagic b-Coloring and Its Application for STGNN Time Series Forecasting on Horizontal Farming by R. Sunder, Ika Hesti Agustin, Dafik Dafik, Ika Nur Maylisa, N. Mohanapriya, Marsidi Marsidi

“…Furthermore, to see the robust application of local antimagic coloring, at the end of this paper we will analyse the implementation of local antimagic coloring on Graph Neural Networks (GNN) multi-step time series forecasting on for NPK (Nitrogen, Phosphorus, and Potassium) concentration of companion plantations.…”
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Forecasting model to predict the first occurrence of Scaphoideus titanus nymphal stages in selected locations in Austria by Kerstin Kolkmann, Gudrun Strauss, Josef Eitzinger, Sylvia Blümel

“…Monitoring data from 2013 to 2020 from six different wine-growing areas in Austria were analysed. The existing forecasting models examined in this study predicted the first seasonal occurrence of N1 on average 3.3 days too early or 5.8 days too late, whereas the newly developed multiple linear regression model (MLR) for N1 predicted the first seasonal occurrence on average 3.4 days too early. …”
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Structural time series modelling for weekly forecasting of enterovirus outpatient, inpatient, and emergency department visits. by Cathy W S Chen, Leon L Hsieh, Betty X Y Chu

“…The objectives are to understand infection patterns through model fitting, forecast future visits for proactive epidemic management, and improve forecast accuracy by incorporating holiday effects. …”
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Real Implementation and Testing of Short-Term Building Load Forecasting: A Comparison of SVR and NARX by Juan José Hernández, Irati Zapirain, Haritza Camblong, Nora Barroso, Octavian Curea

“…These models predict day-ahead hourly electricity consumption using forecasted meteorological data from Meteo Galicia (MG) and building occupancy data, both automatically obtained and pre-processed. …”
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Time Series Forecasting for Air Quality with Structured and Unstructured Data Using Artificial Neural Networks by Kenneth Chan, Paul Matthews, Kamran Munir

“…Various machine learning algorithms exist to predict air quality, but they can only analyse structured data gathered from monitoring stations. …”
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Predictions of COVID-19 dynamics in the UK: Short-term forecasting and analysis of potential exit strategies. by Matt J Keeling, Edward M Hill, Erin E Gorsich, Bridget Penman, Glen Guyver-Fletcher, Alex Holmes, Trystan Leng, Hector McKimm, Massimiliano Tamborrino, Louise Dyson, Michael J Tildesley

“…Government agencies are presently analysing how best to develop an exit strategy from these measures and to determine how the epidemic may progress once measures are lifted. …”
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Predictive Model to Analyse Real and Synthetic Data for Learners' Performance Prediction Using Regression Techniques by SHABNAM ARA S.J, Tanuja R, Manjula S.H

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Benefits of Multi-Terminal HVdc Under Extreme Conditions via Production Cost Modeling Analyses by Quan Nguyen, Hongyan Li, Pavel Etingov, Marcelo Elizondo, Jinxiang Zhu, Xinda Ke

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Analysing the atmospheric-oceanic conditions driving the sustained long track and intensity of Tropical Cyclone Freddy by Zaine Perry, Ramontsheng Rapolaki, Sarah Roffe, Moagabo Ragoasha

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Modeling and Forecasting of Monthly Global Price of Bananas Using Seasonal ARIMA and Multilayer Perceptron Neural Network by Yeong Nain Chi, Orson Chi

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Real-Time Run-Off-Road Risk Prediction Based on Deep Learning Sequence Forecasting Approach by Yunteng Chen, Lijun Wei, Qiong Bao, Huansong Zhang

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A Consistency-Aware Hybrid Static–Dynamic Multivariate Network for Forecasting Industrial Key Performance Indicators by Jiahui Long, Xiang Jia, Bingyi Li, Lin Zhu, Miao Wang

“…The accurate forecasting of key performance indicators (KPIs) is essential for enhancing the reliability and operational efficiency of engineering systems under increasingly complex security challenges. …”
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A Comprehensive Analysis of Dropout Assisted Regularized Deep Learning Architectures for Dynamic Electricity Price Forecasting by Pooja Joshi, Stale Stordal, Gudbrand Lien, Deepti Mishra, Erik Haugom

“…For such applications, data-driven approaches such as deep learning (DL) have shown promising forecasting capabilities in recent years. Therefore, this work comprehensively analyses the performance of various DL architectures for EPF across five major global deregulated electricity markets. …”
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Statistical modelling and forecasting of HIV and anti-retroviral therapy cases by time-series and machine learning models by Abdullah M. Almarashi

“…Descriptive and time series analyses were conducted using the R programming language. …”
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Analysis, Forecasting, and System Identification of a Floating Offshore Wind Turbine Using Dynamic Mode Decomposition by Giorgio Palma, Andrea Bardazzi, Alessia Lucarelli, Chiara Pilloton, Andrea Serani, Claudio Lugni, Matteo Diez

“…All the analyses are performed on experimental data collected from an operating prototype. …”
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Advanced feature engineering in Acute:Chronic Workload Ratio (ACWR) calculation for injury forecasting in elite soccer. by Jaime B Matas-Bustos, Antonio M Mora-García, Moisés de Hoyo Lora, Alejandro Nieto-Alarcón, Francisco T Gonzalez-Fernández

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Energy Services Demand Forecasting Combined with Feature Preferences and Bidirectional Long- and Short-Term Memory Networks by KANG Feng, TAN Huochao, SU Liwei, JIAN Donglin, WANG Shuai, QIN Hao, ZHANG Yongjun

“…Accurate and efficient demand forecasting of customer energy services is crucial for quality and risk management in grid customer service. …”
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Hybrid time series and machine learning models for forecasting cardiovascular mortality in India: an age specific analysis by M Darshan Teja, G Mokesh Rayalu

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Forecast of brine blowout zones in the Amu Darya basin and analysis of brine blowout in oil and gas fields by Deryaev Annaguly

“…The purpose of this study is to forecast possible brine blowout zones in the Amu Darya basin and further analyse these manifestations in oil and gas fields to assess their potential for hydrocarbon extraction. …”
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