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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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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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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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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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Predicting chaotic time series [PDF]
Physical Review Letters, 1987 We present a forecasting technique for chaotic data. After embedding a time series in a state space using delay coordinates, we ``learn'' the induced nonlinear mapping using local approximation. This allows us to make short-term predictions of the future behavior of a time series, using information based only on past values.
, Farmer, , Sidorowich
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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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Conformal Prediction for Time Series
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023 We develop a general framework for constructing distribution-free prediction intervals for time series. Theoretically, we establish explicit bounds on conditional and marginal coverage gaps of estimated prediction intervals, which asymptotically converge to zero under additional assumptions.
Chen Xu, Yao Xie
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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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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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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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