Quantifying the Uncertainty of Reservoir Computing: Confidence Intervals for Time-Series Forecasting
Author
Abstract
Suggested Citation
Download full text from publisher
References listed on IDEAS
- Domingo, L. & Grande, M. & Borondo, F. & Borondo, J., 2023. "Anticipating food price crises by reservoir computing," Chaos, Solitons & Fractals, Elsevier, vol. 174(C).
- Lina Jaurigue & Kathy Lüdge, 2022. "Connecting reservoir computing with statistical forecasting and deep neural networks," Nature Communications, Nature, vol. 13(1), pages 1-3, December.
- Tilmann Gneiting, 2008. "Editorial: Probabilistic forecasting," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 171(2), pages 319-321, April.
- Myles R. Allen & Peter A. Stott & John F. B. Mitchell & Reiner Schnur & Thomas L. Delworth, 2000. "Quantifying the uncertainty in forecasts of anthropogenic climate change," Nature, Nature, vol. 407(6804), pages 617-620, October.
- Poole, C., 1987. "Beyond the confidence interval," American Journal of Public Health, American Public Health Association, vol. 77(2), pages 195-199.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Maneesoonthorn, Worapree & Martin, Gael M. & Forbes, Catherine S. & Grose, Simone D., 2012.
"Probabilistic forecasts of volatility and its risk premia,"
Journal of Econometrics, Elsevier, vol. 171(2), pages 217-236.
- Worapree Maneesoonthorn & Gael M. Martin & Catherine S. Forbes & Simone Grose, 2010. "Probabilistic Forecasts of Volatility and its Risk Premia," Monash Econometrics and Business Statistics Working Papers 22/10, Monash University, Department of Econometrics and Business Statistics.
- Andrew Briggs & Paul Fenn, 1998. "Confidence intervals or surfaces? Uncertainty on the cost‐effectiveness plane," Health Economics, John Wiley & Sons, Ltd., vol. 7(8), pages 723-740, December.
- McDermott, Shana M. & Finnoff, David C. & Shogren, Jason F. & Kennedy, Chris J., 2021. "When does natural science uncertainty translate into economic uncertainty?," Ecological Economics, Elsevier, vol. 184(C).
- Dovern, Jonas & Feldkircher, Martin & Huber, Florian, 2016.
"Does joint modelling of the world economy pay off? Evaluating global forecasts from a Bayesian GVAR,"
Journal of Economic Dynamics and Control, Elsevier, vol. 70(C), pages 86-100.
- Jonas Dovern & Martin Feldkircher & Florian Huber, 2015. "Does Joint Modelling of the World Economy Pay Off? Evaluating Global Forecasts from a Bayesian GVAR," Working Papers 200, Oesterreichische Nationalbank (Austrian Central Bank).
- Dovern, Jonas & Feldkircher, Martin & Huber , Florian, 2015. "Does Joint Modelling of the World Economy Pay Off? Evaluating Global Forecasts from a Bayesian GVAR," Working Papers 0590, University of Heidelberg, Department of Economics.
- Luisa Bisaglia & Matteo Grigoletto, 2021. "A new time-varying model for forecasting long-memory series," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(1), pages 139-155, March.
- Kyle E. Binder & Mohsen Pourahmadi & James W. Mjelde, 2020. "The role of temporal dependence in factor selection and forecasting oil prices," Empirical Economics, Springer, vol. 58(3), pages 1185-1223, March.
- Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilios & Chen, Zhi & Gaba, Anil & Tsetlin, Ilia & Winkler, Robert L., 2022. "The M5 uncertainty competition: Results, findings and conclusions," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1365-1385.
- James Ming Chen, 2018. "On Exactitude in Financial Regulation: Value-at-Risk, Expected Shortfall, and Expectiles," Risks, MDPI, vol. 6(2), pages 1-28, June.
- Alexandra M. Schmidt & Marco A. Rodríguez, 2022. "Discussion on “A combined estimate of global temperature”," Environmetrics, John Wiley & Sons, Ltd., vol. 33(3), May.
- Enrique Moral-Benito, 2015. "Model Averaging In Economics: An Overview," Journal of Economic Surveys, Wiley Blackwell, vol. 29(1), pages 46-75, February.
- Malte Knuppel & Fabian Kruger & Marc-Oliver Pohle, 2022.
"Score-based calibration testing for multivariate forecast distributions,"
Papers
2211.16362, arXiv.org, revised Dec 2023.
- Knüppel, Malte & Krüger, Fabian & Pohle, Marc-Oliver, 2022. "Score-based calibration testing for multivariate forecast distributions," Discussion Papers 50/2022, Deutsche Bundesbank.
- Karol Bednarz & Bartłomiej Garda, 2024. "Measurement and Modeling of Self-Directed Channel (SDC) Memristors: An Extensive Study," Energies, MDPI, vol. 17(21), pages 1-20, October.
- Kruschke, John K. & Liddell, Torrin, 2016. "The Bayesian New Statistics: Hypothesis testing, estimation, meta-analysis, and power analysis from a Bayesian perspective," OSF Preprints ksfyr, Center for Open Science.
- Wei Wei & Leonhard Held, 2014. "Calibration tests for count data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(4), pages 787-805, December.
- Thordis L. Thorarinsdottir & Tilmann Gneiting, 2010. "Probabilistic forecasts of wind speed: ensemble model output statistics by using heteroscedastic censored regression," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 173(2), pages 371-388, April.
- Lillestøl, Jostein & Sinding-Larsen, Richard, 2015. "Best estimate reporting with asymmetric loss," Discussion Papers 2015/7, Norwegian School of Economics, Department of Business and Management Science.
- Ng, Jason & Forbes, Catherine S. & Martin, Gael M. & McCabe, Brendan P.M., 2013.
"Non-parametric estimation of forecast distributions in non-Gaussian, non-linear state space models,"
International Journal of Forecasting, Elsevier, vol. 29(3), pages 411-430.
- Jason Ng & Catherine S. Forbes & Gael M. Martin & Brendan P.M. McCabe, 2011. "Non-Parametric Estimation of Forecast Distributions in Non-Gaussian, Non-linear State Space Models," Monash Econometrics and Business Statistics Working Papers 11/11, Monash University, Department of Econometrics and Business Statistics.
- James W. Taylor & Keming Yu, 2016. "Using auto-regressive logit models to forecast the exceedance probability for financial risk management," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 179(4), pages 1069-1092, October.
- J. S. Butler & Peter Jones, 2018. "Theoretical and empirical distributions of the p value," METRON, Springer;Sapienza Università di Roma, vol. 76(1), pages 1-30, April.
- Gneiting, Tilmann, 2011. "Making and Evaluating Point Forecasts," Journal of the American Statistical Association, American Statistical Association, vol. 106(494), pages 746-762.
More about this item
Keywords
reservoir computing; uncertainty; confidence intervals; time series; market; prices;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:12:y:2024:i:19:p:3078-:d:1490304. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.