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Rodgers Makwinja, Seyoum Mengistou, Emmanuel Kaunda, Tena Alemiew, Titus Bandulo Phiri, Ishmael Bobby Mphangwe Kosamu and Chikumbusko Chiziwa Kaonga
Forecasting, using time series data, has become the most relevant and effective tool for fisheries stock assessment. Autoregressive integrated moving average (ARIMA) modeling has been commonly used to predict the general trend for fish landings with incr...
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Elena Pagano and Enrico Barbierato
Air pollution is a paramount issue, influenced by a combination of natural and anthropogenic sources, various diffusion modes, and profound repercussions for the environment and human health. Herein, the power of time series data becomes evident, as it p...
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Sebastian C. Ibañez and Christopher P. Monterola
Accurate prediction of crop production is essential in effectively managing the food security and economic resilience of agricultural countries. This study evaluates the performance of statistical and machine learning-based methods for large-scale crop p...
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Amal Al Ali, Ahmed M. Khedr, Magdi El Bannany and Sakeena Kanakkayil
Despite the obvious benefits and growing popularity of Machine Learning (ML) technology, there are still concerns regarding its ability to provide Financial Distress Prediction (FDP). An accurate FDP model is required to avoid financial risk at the lowes...
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Kamel Arafet and Rafael Berlanga
The generation of electricity through renewable energy sources increases every day, with solar energy being one of the fastest-growing. The emergence of information technologies such as Digital Twins (DT) in the field of the Internet of Things and Indust...
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Sichen Li, Mélissa Zacharias, Jochem Snuverink, Jaime Coello de Portugal, Fernando Perez-Cruz, Davide Reggiani and Andreas Adelmann
The beam interruptions (interlocks) of particle accelerators, despite being necessary safety measures, lead to abrupt operational changes and a substantial loss of beam time. A novel time series classification approach is applied to decrease beam time lo...
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Wei Xiang, Rui Zhang, Guoxiang Liu, Xiaowen Wang, Wenfei Mao, Bo Zhang, Yin Fu and Tingting Wu
Significant seasonal fluctuations could occur in the regional scattering characteristics and surface deformation of saline soil, and cause decorrelation, which limits the application of the conventional time-series InSAR (TS-InSAR). For extending the sal...
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Matteo Taroni, Giorgio Vocalelli and Andrea De Polis
We introduce a novel approach to estimate the temporal variation of the b-value parameter of the Gutenberg?Richter law, based on the weighted likelihood approach. This methodology allows estimating the b-value based on the full history of the available d...
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Andrea Brunello, Enrico Marzano, Angelo Montanari and Guido Sciavicco
Temporal information plays a very important role in many analysis tasks, and can be encoded in at least two different ways. It can be modeled by discrete sequences of events as, for example, in the business intelligence domain, with the aim of tracking t...
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P. L. DISSANAYAKE, S. S. N. PERERA
Pág. Page:295 - 305Abstrac
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Brima Ibrahim Baimba Kargbo,Festus O. Egwaikhide
Pág. 432 - 447
The fiscal authorities in Sierra Leone introduced series of reforms in the tax system ranging from continual revisions in tax rate to harmonization and instituting new taxes that are relatively easy to collect. Despite these measures, the output of the t...
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Carlos Garcia Calatrava, Yolanda Becerra Fontal and Fernando M. Cucchietti
Time series databases aim to handle big amounts of data in a fast way, both when introducing new data to the system, and when retrieving it later on. However, depending on the scenario in which these databases participate, reducing the number of requeste...
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Konstantinos I. Papageorgiou, Katarzyna Poczeta, Elpiniki Papageorgiou, Vassilis C. Gerogiannis and George Stamoulis
This paper introduced a new ensemble learning approach, based on evolutionary fuzzy cognitive maps (FCMs), artificial neural networks (ANNs), and their hybrid structure (FCM-ANN), for time series prediction. The main aim of time series forecasting is to ...
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Kyriakos Apostolidis, Christos Kokkotis, Evangelos Karakasis, Evangeli Karampina, Serafeim Moustakidis, Dimitrios Menychtas, Georgios Giarmatzis, Dimitrios Tsiptsios, Konstantinos Vadikolias and Nikolaos Aggelousis
Stroke remains a predominant cause of mortality and disability worldwide. The endeavor to diagnose stroke through biomechanical time-series data coupled with Artificial Intelligence (AI) poses a formidable challenge, especially amidst constrained partici...
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Fatma Yaprakdal and Merve Varol Arisoy
In the smart grid paradigm, precise electrical load forecasting (ELF) offers significant advantages for enhancing grid reliability and informing energy planning decisions. Specifically, mid-term ELF is a key priority for power system planning and operati...
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Dung David Chuwang and Weiya Chen
Forecasting daily and weekly passenger demand is a key fundamental process used by existing urban rail transit (URT) station authorities to diagnose operational problems and make decisions about train schedule patterns to improve operational efficiency, ...
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Chunyang Wang, Huan Zhang, Xifang Wu, Wei Yang, Yanjun Shen, Bibo Lu and Jianlong Wang
Accurate and rapid access to crop distribution information is a significant requirement for the development of modern agriculture. Improving the efficiency of remote sensing monitoring of winter wheat planting area information, a new method of automatica...
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Lianwei Li, Yangfeng Xu, Cunjin Xue, Yuxuan Fu and Yuanyu Zhang
It is important to consider where, when, and how the evolution of sea surface temperature anomalies (SSTA) plays significant roles in regional or global climate changes. In the comparison of where and when, there is a great challenge in clearly describin...
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K. Kalidas, K. Mahendran, K. Akila
Pág. Page:59 - 66Abstract
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Kamil Kowalczyk and Jacek Rapinski
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