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Jeffrey Tim Query, Evaristo Diz
Pág. 145 - 159
AbstractIn this study we examine the robustness of fit for a multivariate and an autoregressive integrated moving average model to a data sample time series type. The sample is a recurrent actuarial data set for a 10-year horizon. We utilize ...
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Kun Zhang, Jianyao Yao, Wenxiang Zhu, Zhifu Cao, Teng Li and Jianqiang Xin
The thermal protection system (TPS) represents one of the most critical subsystems for vehicle re-entry. However, due to uncertainties in thermal loads, material properties, and manufacturing deviations, the thermal response of the TPS exhibits significa...
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C. Tamilselvi, Md Yeasin, Ranjit Kumar Paul and Amrit Kumar Paul
Denoising is an integral part of the data pre-processing pipeline that often works in conjunction with model development for enhancing the quality of data, improving model accuracy, preventing overfitting, and contributing to the overall robustness of pr...
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Gregorius Ryan, Pricillia Katarina and Derwin Suhartono
The rise of social media as a platform for self-expression and self-understanding has led to increased interest in using the Myers?Briggs Type Indicator (MBTI) to explore human personalities. Despite this, there needs to be more research on how other wor...
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Lan Wang, Mingjiang Xie, Min Pan, Feng He, Bing Yang, Zhigang Gong, Xuke Wu, Mingsheng Shang and Kun Shan
Harmful algal blooms (HABs) have been deteriorating global water bodies, and the accurate prediction of algal dynamics using the modelling method is a challenging research area. High-frequency monitoring and deep learning technology have opened up new ho...
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Cindy Trinh, Silvia Lasala, Olivier Herbinet and Dimitrios Meimaroglou
This article investigates the applicability domain (AD) of machine learning (ML) models trained on high-dimensional data, for the prediction of the ideal gas enthalpy of formation and entropy of molecules via descriptors. The AD is crucial as it describe...
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Demetris Koutsoyiannis and Alberto Montanari
Bluecat is a recently proposed methodology to upgrade a deterministic model (D-model) into a stochastic one (S-model), based on the hypothesis that the information contained in a time series of observations and the concurrent predictions made by the D-mo...
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Katleho Makatjane and Tshepiso Tsoku
This study aims to overcome the problem of dimensionality, accurate estimation, and forecasting Value-at-Risk (VaR) and Expected Shortfall (ES) uncertainty intervals in high frequency data. A Bayesian bootstrapping and backtest density forecasts, which a...
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Yuan Chen and Abdul Q. M. Khaliq
The Lee?Carter model could be considered as one of the most important mortality prediction models among stochastic models in the field of mortality. With the recent developments of machine learning and deep learning, many studies have applied deep learni...
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Md Monowar Hossain, A. H. M. Faisal Anwar, Nikhil Garg, Mahesh Prakash and Mohammed Bari
Early prediction of rainfall is important for the planning of agriculture, water infrastructure, and other socio-economic developments. The near-term prediction (e.g., 10 years) of hydrologic data is a recent development in GCM (General Circulation Model...
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