Reference health

Probabilistic Forecasting of Sensory Data With Generative Adversarial Networks – ForGAN

https://doi.org/10.1109/access.2019.2915544
CiteStamped reference-health badge
61/61 checkable references clean · checked 2026-07-23

Every reference with a DOI in the deposited reference list resolved to a known work in Crossref or DataCite at the dated check, and none carried a retraction, withdrawal, or removal notice.

41 without a DOI — not checked. A reference deposited without a DOI is never matched by title or guessed at; it stays outside the checked set, and this line discloses that.

The 61 checked references that resolve
resolves10.1016/j.eswa.2012.05.040
Fuzzy time series forecasting with a novel hybrid approach combining fuzzy c-means and neural networks
resolves10.1016/0165-0114(94)00315-X
A new fuzzy time-series model of fuzzy number observations
resolves10.1007/s10489-014-0529-x
A modified genetic algorithm for forecasting fuzzy time series
resolves10.1016/j.inffus.2006.10.009
A new boosting algorithm for improved time-series forecasting with recurrent neural networks
resolves10.1016/j.asoc.2012.05.002
A new time invariant fuzzy time series forecasting method based on particle swarm optimization
resolves10.1016/j.eswa.2011.12.036
Fuzzy based trend mapping and forecasting for time series data
resolves10.1016/S0957-4174(00)00027-0
Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index
resolves10.1016/j.apm.2011.09.066
A hybrid algorithm to optimize RBF network architecture and parameters for nonlinear time series prediction
resolves10.1016/j.procs.2013.05.281
A Novel Stock Forecasting Model based on Fuzzy Time Series and Genetic Algorithm
resolves10.1109/CCECE.2013.6567685
A comparison between SVM and LSSVM in mid-term electricity market clearing price forecasting
resolves10.1016/j.ijforecast.2010.02.007
A heuristic method for parameter selection in LS-SVM: Application to time series prediction
resolves10.1049/iet-gtd.2013.0610
Mid‐term electricity market clearing price forecasting using multiple least squares support vector machines
resolves10.1109/TAI.2003.1250182
Feature selection for support vector machines by means of genetic algorithm
resolves10.1109/2.294849
Genetic algorithms: a survey
resolves10.1007/3-540-45517-5_44
Genetic and Evolutionary Algorithms for Time Series Forecasting
resolves10.7551/mitpress/1090.001.0001
Adaptation in Natural and Artificial Systems
resolves10.1016/j.eswa.2005.09.024
A GA-based feature selection and parameters optimizationfor support vector machines
resolves10.1002/1099-131X(200007)19:4<231::AID-FOR771>3.0.CO;2-#
Density forecasting in economics and finance
resolves10.1017/S1049096512000959
Ensemble Predictions of the 2012 US Presidential Election
resolves10.2307/1913643
Regression Quantiles
resolves10.1017/CBO9780511754098
Quantile Regression
resolves10.1126/science.1104635
Systems Biology and New Technologies Enable Predictive and Preventative Medicine
resolves10.1111/j.1467-985X.2011.01019.x
Improved Probabilistic Prediction of Healthcare Performance Indicators using Bidirectional Smoothing Models
resolves10.1073/pnas.1211452109
Bayesian probabilistic population projections for all countries
resolves10.1214/07-AOAS111
Probabilistic projections of HIV prevalence using Bayesian melding
resolves10.1098/rsta.2007.2068
Ensembles and probabilities: a new era in the prediction of climate change
resolves10.1109/eScience.2018.00130
DeepDownscale: A Deep Learning Strategy for High-Resolution Weather Forecast
resolves10.1080/07350015.2012.727718
Real-Time Inflation Forecasting in a Changing World
resolves10.1029/2018JB016674
PhaseLink: A Deep Learning Approach to Seismic Phase Association
resolves10.1109/TPWRS.2018.2794541
Model-Free Renewable Scenario Generation Using Generative Adversarial Networks
resolves10.1016/S0022-1694(01)00420-6
The case for probabilistic forecasting in hydrology
resolves10.1111/j.1751-5823.2011.00168.x
Short‐Term Wind Speed Forecasting for Power System Operations
resolves10.1214/13-STS445
Wind Energy: Forecasting Challenges for Its Operational Management
resolves10.1256/0035900021643593
The economic value of ensemble forecasts as a tool for risk assessment: From days to decades
resolves10.1126/science.1115255
Weather Forecasting with Ensemble Methods
resolves10.1016/j.jhydrol.2009.06.005
Ensemble flood forecasting: A review
resolves10.1002/qj.1923
Towards the probabilistic Earth‐system simulator: a vision for the future of climate and weather prediction
resolves10.1038/nature14541
Probabilistic machine learning and artificial intelligence
resolves10.2307/1911031
Asymmetric Least Squares Estimation and Testing
resolves10.1111/j.1467-9868.2008.00651.x
Non-Crossing Non-Parametric Estimates of Quantile Curves
resolves10.1016/j.csda.2010.11.015
Geoadditive expectile regression
resolves10.1007/s10182-012-0198-1
Simultaneous estimation of quantile curves using quantile sheets
resolves10.1175/MWR2906.1
Using Bayesian Model Averaging to Calibrate Forecast Ensembles
resolves10.1023/A:1007665907178
An Introduction to Variational Methods for Graphical Models
resolves10.1007/978-1-4757-3437-9_1
An Introduction to Sequential Monte Carlo Methods
resolves10.1111/j.1468-0394.2010.00568.x
Multi‐scale Internet traffic forecasting using neural networks and time series methods
resolves10.1126/science.267326
Oscillation and Chaos in Physiological Control Systems
resolves10.1109/SmartGridComm.2018.8587464
Generative Adversarial Network for Synthetic Time Series Data Generation in Smart Grids
resolves10.1214/aoms/1177729694
On Information and Sufficiency
resolves10.7208/chicago/9780226429823.001.0001
In the Wake of Chaos
resolves10.1007/978-3-319-47054-2_16
A Competitive Modular Neural Network for Long-Term Time Series Forecasting
resolves10.1109/BIBM.2017.8217669
Improving palliative care with deep learning
resolves10.1016/j.inffus.2018.09.012
Machine learning for integrating data in biology and medicine: Principles, practice, and opportunities
resolves10.1146/annurev-statistics-062713-085831
Probabilistic Forecasting
resolves10.1111/rssb.12017
Conditional Transformation Models
resolves10.1109/ICASSP.2018.8462018
Voice Impersonation Using Generative Adversarial Networks
resolves10.1177/1471082X13494159
Beyond mean regression
resolves10.18653/v1/D17-1230
Adversarial Learning for Neural Dialogue Generation
resolves10.1109/ICASSP.2018.8462581
Exploring Speech Enhancement with Generative Adversarial Networks for Robust Speech Recognition
resolves10.21437/Interspeech.2017-1620
Conditional Generative Adversarial Networks for Speech Enhancement and Noise-Robust Speaker Verification
resolves10.1109/ICASSP.2018.8462091
SVSGAN: Singing Voice Separation Via Generative Adversarial Network
The 41 references without a DOI — listed, not checked
no DOI — not checkedref39
no DOI — not checkedNeural networks for time series processing
no DOI — not checkedApplication of fuzzy multiple attribute decision making on company analysis for stock selection
no DOI — not checkedNonlinear systems identification using deep dynamic neural networks
no DOI — not checkedref23
no DOI — not checkedRegression percentiles using asymmetric squared error loss
no DOI — not checkedThe transition from point to distribution estimation
no DOI — not checkedExtremeweather: A large-scale climate dataset for semi-supervised detection, localization, and understanding of extreme weather events
no DOI — not checkedCRED: A deep residual network of convolutional and recurrent units for earthquake signal detection
no DOI — not checkedML for flood forecasting at scale
no DOI — not checkedA multi-scheme ensemble using coopetitive soft-gating with application to power forecasting for renewable energy generation
no DOI — not checkedOperational earthquake forecasting. State of knowledge and guidelines for utilization
no DOI — not checkedUncertainty in deep learning
no DOI — not checkedDeep Gaussian processes
no DOI — not checkedDropout: A simple way to prevent neural networks from overfitting
no DOI — not checkedWasserstein GAN
no DOI — not checkedAdversarial feature learning
no DOI — not checkedGenerative adversarial nets
no DOI — not checkedUnsupervised representation learning with deep convolutional generative adversarial networks
no DOI — not checkedInfoGAN: Interpretable representation learning by information maximizing generative adversarial nets
no DOI — not checkedConditional generative adversarial nets
no DOI — not checkedDropout as a Bayesian approximation: Representing model uncertainty in deep learning
no DOI — not checkedProbabilistic inference using Markov chain Monte Carlo methods
no DOI — not checkedExpectation propagation for approximate Bayesian inference
no DOI — not checkedref95
no DOI — not checkedref94
no DOI — not checkedref93
no DOI — not checkedAnomaly detection with generative adversarial networks for multivariate time series
no DOI — not checkedReal-valued (medical) time series generation with recurrent conditional gans
no DOI — not checkedref102
no DOI — not checkedOptimal data-based binning for histograms
no DOI — not checkedAccurate, data-efficient learning from noisy, choice-based labels for inherent risk scoring
no DOI — not checkedBenchmarking deep sequential models on volatility predictions for financial time series
no DOI — not checkedPredicting inpatient discharge prioritization with electronic health records
no DOI — not checkedMachine learning on electronic health records: Models and features usages to predict medication non-adherence
no DOI — not checkedSeqgan: Sequence generative adversarial nets with policy gradient
no DOI — not checkedref19
no DOI — not checkedLanguage generation with recurrent generative adversarial networks without pre-training
no DOI — not checkedC-RNN-GAN: Continuous recurrent neural networks with adversarial training
no DOI — not checkedAdversarial audio synthesis
no DOI — not checkedGenerating text via adversarial training
What this badge says. CiteStamped means the CHECKABLE references of this work were clean at the dated check: each resolved to a known work in a public registry, and none carried a retraction notice at that time. It says nothing about the quality, findings, or importance of the work itself, and nothing about references deposited without a DOI.

checked 2026-07-23 — re-checked daily as this page is visited; titles and statuses come from Crossref and DataCite and are not part of the signed record

Embed this badge

Both snippets point at the live badge image and link back to this page. The badge re-renders from the daily check, so an embed never goes stale by more than a day of visits.

<a href="https://citestamp.com/citestamped/10.1109/access.2019.2915544"><img src="https://citestamp.com/citestamped/10.1109/access.2019.2915544/badge.svg" alt="CiteStamped reference-health badge" width="460" height="64"></a>
[![CiteStamped reference-health badge](https://citestamp.com/citestamped/10.1109/access.2019.2915544/badge.svg)](https://citestamp.com/citestamped/10.1109/access.2019.2915544)