Zobrazeno 1 - 10
of 820
pro vyhledávání: '"series decomposition"'
Autor:
Erick Estrada-Patiño, Guadalupe Castilla-Valdez, Juan Frausto-Solis, Javier González-Barbosa, Juan Paulo Sánchez-Hernández
Publikováno v:
International Journal of Computational Intelligence Systems, Vol 17, Iss 1, Pp 1-18 (2024)
Abstract This paper presents FORSEER (Forecasting by Selective Ensemble Estimation and Reconstruction), a novel methodology designed to address temperature forecasting under the challenges inherent to climate change. FORSEER integrates decomposition,
Externí odkaz:
https://doaj.org/article/5e6e71559d1741ee874669cb47a27856
Autor:
Chunrong Chen, Zhaoyuan He, Jin Zhao, Xuhui Zhu, Jiabao Li, Xinnan Wu, Zhongting Chen, Hailan Chen, Gengjie Jia
Publikováno v:
BMC Infectious Diseases, Vol 24, Iss 1, Pp 1-14 (2024)
Abstract Background Zoonotic infections, characterized with huge pathogen diversity, wide affecting area and great society harm, have become a major global public health problem. Early and accurate prediction of their outbreaks is crucial for disease
Externí odkaz:
https://doaj.org/article/2f75443c8b6f45d19a40f09c0748ff86
Autor:
Shujun Wu, Zengchuan Dong, Sandra M. Guzmán, Gregory Conde, Wenzhuo Wang, Shengnan Zhu, Yiqing Shao, Jinyu Meng
Publikováno v:
Ecological Informatics, Vol 84, Iss , Pp 102914- (2024)
Runoff is pivotal in water resource management and ecological conservation. Current research predominantly emphasizes enhancing the precision of machine learning-based runoff predictions, with limited focus on their physical interpretability. This st
Externí odkaz:
https://doaj.org/article/3e35cd4e106d469c9147b3a10106517a
Publikováno v:
International Journal of Electrical Power & Energy Systems, Vol 161, Iss , Pp 110190- (2024)
In the field of time series classification, deep learning techniques have shown remarkable performance; however, their effectiveness is often compromised when confronted with challenges of insufficient data and class imbalance. To address this challe
Externí odkaz:
https://doaj.org/article/4edb959cef8a4091aba60524f3b88a7f
Autor:
Shuhua Gao, Yuanbin Liu, Jing Wang, Zhengfang Wang, Xu Wenjun, Renfeng Yue, Ruipeng Cui, Yong Liu, Xuezhong Fan
Publikováno v:
International Journal of Electrical Power & Energy Systems, Vol 164, Iss , Pp 110349- (2025)
Accurate load forecasting plays a crucial role in the optimal scheduling of electric vehicles’ (EVs) coordinated charging. Although many load forecasting methods have emerged in recent years, these methods face two significant challenges: effective
Externí odkaz:
https://doaj.org/article/290ee7d60ef145dbaf5fad953db8bd8c
Publikováno v:
Forecasting, Vol 6, Iss 1, Pp 204-223 (2024)
In forecasting research, the focus has largely been on decision support systems for enhancing performance, with fewer studies in learning support systems. As a remedy, Intelligent Tutoring Systems (ITSs) offer an innovative solution in that they prov
Externí odkaz:
https://doaj.org/article/dbcd494142574a59ac7b57dfbaedd2c6
Publikováno v:
Journal of Safety Science and Resilience, Vol 5, Iss 1, Pp 13-36 (2024)
Recent years have seen increasing academic interest in exploring the correlation between temperature and crime. However, it is uncertain whether similar long-term trends or seasonality (rather than causal effect) of temperature and crime is the major
Externí odkaz:
https://doaj.org/article/bd5c7c3dd8a4492c93bce001d7eebba9
Publikováno v:
Mathematical Biosciences and Engineering, Vol 21, Iss 2, Pp 3391-3421 (2024)
An accurate ultra-short-term time series prediction of a power load is an important guarantee for power dispatching and the safe operation of power systems. Problems of the current ultra-short-term time series prediction algorithms include low predic
Externí odkaz:
https://doaj.org/article/7dccc02e2957476880cc77811f0ad5a7
Publikováno v:
IEEE Access, Vol 12, Pp 155340-155350 (2024)
This study introduces an innovative air quality prediction model, TD-CS-Transformer, which fuses time series decomposition and convolutional sparse self-attention Transformer model. It solves inefficient and limited long-distance dependency capture o
Externí odkaz:
https://doaj.org/article/3367fcc456fd47859df6dffbcfb8df5f
Publikováno v:
Applied Sciences, Vol 14, Iss 21, p 10021 (2024)
Land subsidence (LS) is a geological hazard driven by both natural conditions and human activities. Traditional LS time-series prediction models often struggle to accurately capture nonlinear data characteristics, leading to suboptimal predictions. T
Externí odkaz:
https://doaj.org/article/8eee234378d94ef08d1c55ee5258b58e