G. Papacharalampous, H. Tyralis, and D. Koutsoyiannis, Univariate time series forecasting of temperature and precipitation with a focus on machine learning algorithms: a multiple-case study from Greece, Water Resources Management, 32 (15), 5207–5239, doi:10.1007/s11269-018-2155-6, 2018.
[doc_id=1920]
[Αγγλικά]
Το πλήρες κείμενο διατίθεται μόνο στο δίκτυο του ΕΜΠ λόγω νομικών περιορισμών
Συμπληρωματικό υλικό:
Εργασίες μας στις οποίες αναφέρεται αυτή η εργασία:
1. | D. Koutsoyiannis, H. Yao, and A. Georgakakos, Medium-range flow prediction for the Nile: a comparison of stochastic and deterministic methods, Hydrological Sciences Journal, 53 (1), 142–164, doi:10.1623/hysj.53.1.142, 2008. |
2. | H. Tyralis, and D. Koutsoyiannis, Simultaneous estimation of the parameters of the Hurst-Kolmogorov stochastic process, Stochastic Environmental Research & Risk Assessment, 25 (1), 21–33, 2011. |
3. | G. Papacharalampous, H. Tyralis, and D. Koutsoyiannis, Forecasting of geophysical processes using stochastic and machine learning algorithms, European Water, 59, 161–168, 2017. |
4. | H. Tyralis, and G. Papacharalampous, Variable selection in time series forecasting using random forests, Algorithms, 10, 114, doi:10.3390/a10040114, 2017. |
Εργασίες μας που αναφέρονται σ' αυτή την εργασία:
1. | G. Papacharalampous, H. Tyralis, A. Langousis, A. W. Jayawardena, B. Sivakumar, N. Mamassis, A. Montanari, and D. Koutsoyiannis, Probabilistic hydrological post-processing at scale: Why and how to apply machine-learning quantile regression algorithms, Water, doi:10.3390/w11102126, 2019. |
2. | G. Papacharalampous, H. Tyralis, D. Koutsoyiannis, and A. Montanari, Quantification of predictive uncertainty in hydrological modelling by harnessing the wisdom of the crowd: A large-sample experiment at monthly timescale, Advances in Water Resources, 136, 103470, doi:10.1016/j.advwatres.2019.103470, 2020. |
Άλλες εργασίες που αναφέρονται σ' αυτή την εργασία: Δείτε τις στο Google Scholar ή στο ResearchGate