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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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Time Series Segmentation Based on Stationarity Analysis to Improve New Samples Prediction
Sensors, 2021 A wide range of applications based on sequential data, named time series, have become increasingly popular in recent years, mainly those based on the Internet of Things (IoT).
Ricardo Petri Silva +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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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 0002
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On predictability of time series [PDF]
Physica A: Statistical Mechanics and its Applications, 2019 The method to estimate the predictability of human mobility was proposed in [C. Song \emph{et al.}, Science {\bf 327}, 1018 (2010)], which is extensively followed in exploring the predictability of disparate time series. However, the ambiguous description in the original paper leads to some misunderstandings, including the inconsistent logarithm bases ...
Xu, Paiheng +3 more
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Prediction for discrete time series [PDF]
Probability Theory and Related Fields, 2004 Let $\{X_n\}$ be a stationary and ergodic time series taking values from a finite or countably infinite set ${\cal X}$. Assume that the distribution of the process is otherwise unknown. We propose a sequence of stopping times $λ_n$ along which we will be able to estimate the conditional probability $P(X_{λ_n+1}=x|X_0,...,X_{λ_n})$ from data segment ...
Gusztáv Morvai, Benjamin Weiss 0002
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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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