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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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Aymane Ahajjam, Jaakko Putkonen, Emmanuel Chukwuemeka, Robert Chance and Timothy J. Pasch
Local weather forecasts in the Arctic outside of settlements are challenging due to the dearth of ground-level observation stations and high computational costs. During winter, these forecasts are critical to help prepare for potentially hazardous weathe...
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Mohammad Barooni and Deniz Velioglu Sogut
The design and optimization of floating offshore wind turbines (FOWTs) pose significant challenges, stemming from the complex interplay among aerodynamics, hydrodynamics, structural dynamics, and control systems. In this context, this study introduces an...
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Yang Liu and Qianqian Zhang
Analyzing 165 data from five national control sites in Baiyangdian Lake, this study unveils its spatiotemporal pattern of water quality. Utilizing machine learning and multivariate statistical techniques, this study elucidates the effects of rainfall and...
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Qianyang Li and Xingjun Zhang
For time series forecasting, multivariate grey models are excellent at handling incomplete or vague information. The GM(1, N) model represents this group of models and has been widely used in various fields. However, constructing a meaningful GM(1, N) mo...
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Kidoo Park, Yeongjeong Seong, Younghun Jung, Ilro Youn and Cheon Kyu Choi
The methods for improving the accuracy of water level prediction were proposed in this study by selecting the Gated Recurrent Unit (GRU) model, which is effective for multivariate learning at the Paldang Bridge station in Han River, South Korea, where th...
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Zheren Liu, Chaogui Kang and Xiaoyue Xing
Similar time series search is one of the most important time series mining tasks in our daily life. As recent advances in sensor technologies accumulate abundant multi-dimensional time series data associated with multivariate quantities, it becomes a pri...
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Chenxi Liu, Israel Cohen, Rotem Vishinkin and Hossam Haick
Tuberculosis (TB) has long been recognized as a significant health concern worldwide. Recent advancements in noninvasive wearable devices and machine learning (ML) techniques have enabled rapid and cost-effective testing for the real-time detection of TB...
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Nika Nizharadze, Arash Farokhi Soofi and Saeed Manshadi
Predicting the price gap between the day-ahead Market (DAM) and the real-time Market (RTM) plays a vital role in the convergence bidding mechanism of Independent System Operators (ISOs) in wholesale electricity markets. This paper presents a model to pre...
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Yuanfeng Lian, Yueyao Geng and Tian Tian
Due to the complexity of the oil and gas station system, the operational data, with various temporal dependencies and inter-metric dependencies, has the characteristics of diverse patterns, variable working conditions and imbalance, which brings great ch...
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