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Connector based short time series prediction [PDF]
Scientific ReportsThe limited nature of short series presents difficulties for classical prediction models, as each may only contain partial information about the underlying pattern. A straightforward solution would be to concatenate these short series into longer ones in
Wenjuan Gao +3 more
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Fuzzy inference-based LSTM for long-term time series prediction [PDF]
Scientific Reports, 2023 Long short-term memory (LSTM) based time series forecasting methods suffer from multiple limitations, such as accumulated error, diminishing temporal correlation, and lacking interpretability, which compromises the prediction performance.
Weina Wang +2 more
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Hydrological time series prediction based on IWOA-ALSTM [PDF]
Scientific ReportsThe prediction of hydrological time series is of great significance for developing flood and drought prevention approaches and is an important component in research on smart water resources.
Xuejie Zhang +4 more
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Deterministic reservoir computing for chaotic time series prediction [PDF]
Scientific ReportsReservoir Computing was shown in recent years to be useful as efficient to learn networks in the field of time series tasks. Their randomized initialization, a computational benefit, results in drawbacks in theoretical analysis of large random graphs ...
Johannes Viehweg +2 more
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Chaotic Time Series Prediction Using Rough-Neural Networks [PDF]
Mathematics Interdisciplinary Research, 2023 Artificial neural networks with amazing properties, such as universal approximation, have been utilized to approximate the nonlinear processes in many fields of applied sciences.
Ghasem Ahmadi, Mohammad Dehghandar
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Robust Interval Prediction of Intermittent Demand for Spare Parts Based on Tensor Optimization
Sensors, 2023 Demand for spare parts, which is triggered by element failure, project schedule and reliability demand, etc., is a kind of sensing data to the aftermarket service of large manufacturing enterprises.
Kairong Hong +4 more
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Temperature Time Series Prediction Model Based on Time Series Decomposition and Bi-LSTM Network
Mathematics, 2023 Utilizing a temperature time-series prediction model to achieve good results can help us to accurately sense the changes occurring in temperature levels in advance, which is important for human life.
Kun Zhang, Xing Huo, Kun Shao
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Review of Deep Learning Applied to Time Series Prediction [PDF]
Jisuanji kexue yu tansuo, 2023 The time series is generally a set of random variables that are observed and collected at a certain frequency in the course of something??s development.
LIANG Hongtao, LIU Shuo, DU Junwei, HU Qiang, YU Xu
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Time Series Prediction Based on LSTM-Attention-LSTM Model
IEEE Access, 2023 Time series forecasting uses data from the past periods of time to predict future information, which is of great significance in many applications. Existing time series forecasting methods still have problems such as low accuracy when dealing with some ...
Xianyun Wen, Weibang Li
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Self-Attentive Moving Average for Time Series Prediction
Applied Sciences, 2022 Time series prediction has been studied for decades due to its potential in a wide range of applications. As one of the most popular technical indicators, moving average summarizes the overall changing patterns over a past period and is frequently used ...
Yaxi Su, Chaoran Cui, Hao Qu
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