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Tianao Qin, Ruixin Chen, Rufu Qin and Yang Yu
Time series prediction is an effective tool for marine scientific research. The Hierarchical Temporal Memory (HTM) model has advantages over traditional recurrent neural network (RNN)-based models due to its online learning and prediction capabilities. G...
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Qiang Cheng, Yong Cao, Zhifeng Liu, Lingli Cui, Tao Zhang and Lei Xu
The computer numerically controlled (CNC) system is the key functional component of CNC machine tool control systems, and the servo drive system is an important part of CNC systems. The complex working environment will lead to frequent failure of servo d...
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Yajun Wang, Jianping Zhu and Renke Kang
Seasonal?trend-decomposed transformer has empowered long-term time series forecasting via capturing global temporal dependencies (e.g., period-based dependencies) in disentangled temporal patterns. However, existing methods design various auto-correlatio...
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Lexin Zhang, Ruihan Wang, Zhuoyuan Li, Jiaxun Li, Yichen Ge, Shiyun Wa, Sirui Huang and Chunli Lv
This research introduces a novel high-accuracy time-series forecasting method, namely the Time Neural Network (TNN), which is based on a kernel filter and time attention mechanism. Taking into account the complex characteristics of time-series data, such...
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Xianchang Wang, Siyu Dong and Rui Zhang
In the prediction of time series, Empirical Mode Decomposition (EMD) generates subsequences and separates short-term tendencies from long-term ones. However, a single prediction model, including attention mechanism, has varying effects on each subsequenc...
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Eric Hsueh-Chan Lu and You-Ru Lin
With the rise in the Internet of Things (IOT), mobile devices and Location-Based Social Network (LBSN), abundant trajectory data have made research on location prediction more popular. The check-in data shared through LBSN hide information related to lif...
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Qingliang Xiong, Mingping Liu, Yuqin Li, Chaodan Zheng and Suhui Deng
Due to difficulties with electric energy storage, balancing the supply and demand of the power grid is crucial for the stable operation of power systems. Short-term load forecasting can provide an early warning of excessive power consumption for utilitie...
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Yuki Wakatsuki, Hideaki Nakane and Tempei Hashino
The increasing frequency of devastating floods from heavy rainfall?associated with climate change?has made river stage prediction more important. For steep, forest-covered mountainous watersheds, deep-learning models may improve prediction of river stage...
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Kexin Bao, Jinqiang Bi, Miao Gao, Yue Sun, Xuefeng Zhang and Wenjia Zhang
According to the statistics of water transportation accidents, collision accidents are on the rise as the shipping industry has expanded by leaps and bounds, and the water transportation environment has become more complex, which can result in grave cons...
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Shlomo Dubnov
Capturing long-term statistics of signals and time series is important for modeling recurrent phenomena, especially when such recurrences are a-periodic and can be characterized by the approximate repetition of variable length motifs, such as patterns in...
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