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Uncertainty Quantification through Dropout in Time Series Prediction by Echo State Networks

https://doi.org/10.3390/math8081374
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/env.2553openalex-snapshot
Deep echo state networks with uncertainty quantification for spatio‐temporal forecasting
inferredcites →https://doi.org/10.1002/sta4.160openalex-snapshot
An ensemble quadratic echo state network for non‐linear spatio‐temporal forecasting
inferredcites →https://doi.org/10.1007/978-3-030-30508-6_6openalex-snapshot
Improving Reliability of Object Detection for Lunar Craters Using Monte Carlo Dropout
inferredcites →https://doi.org/10.1007/978-3-540-92910-9_14openalex-snapshot
Neural Networks for Time-Series Forecasting
inferredcites →https://doi.org/10.1007/978-3-642-35289-8_36openalex-snapshot
A Practical Guide to Applying Echo State Networks
inferredcites →https://doi.org/10.1007/s12559-017-9461-9openalex-snapshot
Echo State Property of Deep Reservoir Computing Networks
inferredcites →https://doi.org/10.1016/j.cmpb.2019.105150openalex-snapshot
Automatic myocardial segmentation in dynamic contrast enhanced perfusion MRI using Monte Carlo dropout in an encoder-decoder convolutional neural network
inferredcites →https://doi.org/10.1016/j.media.2019.101619openalex-snapshot
Uncertainty and interpretability in convolutional neural networks for semantic segmentation of colorectal polyps
inferredcites →https://doi.org/10.1016/j.neunet.2007.04.016openalex-snapshot
Optimization and applications of echo state networks with leaky- integrator neurons
inferredcites →https://doi.org/10.1016/j.patrec.2018.11.013openalex-snapshot
A novel distance measure for time series: Maximum shifting correlation distance
inferredcites →https://doi.org/10.1109/icdmw.2017.19openalex-snapshot
Deep and Confident Prediction for Time Series at Uber
inferredcites →https://doi.org/10.1109/indin41052.2019.8972125openalex-snapshot
Probabilistic Wind Power Forecasting via Bayesian Deep Learning Based Prediction Intervals
inferredcites →https://doi.org/10.1109/tnnls.2013.2250299openalex-snapshot
Prediction Intervals for a Noisy Nonlinear Time Series Based on a Bootstrapping Reservoir Computing Network Ensemble
inferredcites →https://doi.org/10.1109/tnnls.2019.2899744openalex-snapshot
Augmenting Recurrent Neural Networks Resilience by Dropout
inferredcites →https://doi.org/10.1126/science.1091277openalex-snapshot
Harnessing Nonlinearity: Predicting Chaotic Systems and Saving Energy in Wireless Communication
inferredcites →https://doi.org/10.1145/3278681.3278691openalex-snapshot
Dropout algorithms for recurrent neural networks
inferredcites →https://doi.org/10.1162/neco.1997.9.8.1735openalex-snapshot
Long Short-Term Memory
inferredcites →https://doi.org/10.25080/majora-92bf1922-011openalex-snapshot
Statsmodels: Econometric and Statistical Modeling with Python
inferredcites →https://doi.org/10.3390/e21030305openalex-snapshot
Deep Neural Networks for ECG-Based Pulse Detection during Out-of-Hospital Cardiac Arrest
inferredcites →https://doi.org/10.3390/math8071078openalex-snapshot
Ranking Information Extracted from Uncertainty Quantification of the Prediction of a Deep Learning Model on Medical Time Series Data
inferredcites →https://doi.org/10.3390/s20113032openalex-snapshot
Automated Detection of Presymptomatic Conditions in Spinocerebellar Ataxia Type 2 Using Monte Carlo Dropout and Deep Neural Network Techniques with Electrooculogram Signals

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Improving Flight Trajectory Predictions with Bayesian-Optimized ConvLSTM Models
inferred← citeshttps://doi.org/10.1007/s10707-022-00479-wopenalex-snapshot
Probabilistic air quality forecasting using deep learning spatial–temporal neural network
inferred← citeshttps://doi.org/10.1016/j.compeleceng.2025.110854openalex-snapshot
Ensemble echo state networks trained collectively for reliable energy forecasting in facilities
inferred← citeshttps://doi.org/10.1016/j.geoai.2025.100003openalex-snapshot
Deep learning uncertainty quantification for enhancing TBM operational predictions
inferred← citeshttps://doi.org/10.1016/j.knosys.2026.116766openalex-snapshot
Understanding the role of dropout in Deep Echo State Networks
inferred← citeshttps://doi.org/10.1016/j.oregeorev.2021.104201openalex-snapshot
A comparative study of neural network methods for first break detection using seismic refraction data over a detrital iron ore deposit
inferred← citeshttps://doi.org/10.1016/j.spasta.2023.100732openalex-snapshot
Using echo state networks to inform physical models for fire front propagation
inferred← citeshttps://doi.org/10.1080/10298436.2021.1993219openalex-snapshot
Prediction of flexible pavement 3-D finite element responses using Bayesian neural networks
inferred← citeshttps://doi.org/10.1109/icnc68183.2026.11416946openalex-snapshot
Uncertainty-Guided Bandwidth-Adaptive Model Compression for Industrial Pore Detection
inferred← citeshttps://doi.org/10.1145/3723049openalex-snapshot
SQUIREDL: Sparse Sequence-to-Sequence Uncertainty Estimation in Evidential Deep Learning
inferred← citeshttps://doi.org/10.32469/10355/96967openalex-snapshot
Modeling spatio-temporal data using a Bayesian probabilistic cellular automata framework
inferred← citeshttps://doi.org/10.3390/app15084363openalex-snapshot
Monte Carlo Dropout Neural Networks for Forecasting Sinusoidal Time Series: Performance Evaluation and Uncertainty Quantification
inferred← citeshttps://doi.org/10.3390/biomimetics8030322openalex-snapshot
Design of Intelligent Neuro-Supervised Networks for Brain Electrical Activity Rhythms of Parkinson’s Disease Model
inferred← citeshttps://doi.org/10.3390/forecast6030033openalex-snapshot
Time-Series Interval Forecasting with Dual-Output Monte Carlo Dropout: A Case Study on Durian Exports
inferred← citeshttps://doi.org/10.4018/979-8-2600-3930-4.ch004openalex-snapshot
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