Callegari, M., Mazzoli, P., de Gregorio, L., Notarnicola, C., Pasolli, L.,
Petitta, M., and Pistocchi, A.: Seasonal streamflow forecasting using support
vector regression: a case study in the Italian Alps, Water, 7, 2494–2515,
https://doi.org/10.3390/w7052494, 2015.
De Gregorio, L., Callegari, M., Mazzoli, P., Bagli, S., Broccoli, D.,
Pistocchi, A., and Notarnicola, C.: Operational Streamflow Forecasting with
Support Vector Regression Technique Applied to Alpine Catchments: Results,
Advantages, Limits and Lesson Learned, Water Resour. Manag., 32, 229–242,
https://doi.org/10.1007/s11269-017-1806-3, 2018.
Gagolewski, M.: stringi: Character String Processing Facilities, R package
version 1.2.2, available at:
https://CRAN.R-project.org/package=stringi
(last access: 15 August 2018), 2018.
Fraley, C., Leisch, F., Maechler, M., Reisen, V., and Lemonte, A.: fracdiff:
Fractionally differenced ARIMA aka ARFIMA(p,d,q) models, R package version
1.4-2, available at:
https://CRAN.R-project.org/package=fracdiff (last
access: 15 August 2018), 2012.
Hong, T. and Fan, S.: Probabilistic electric load forecasting: A tutorial
review, Int. J. Forecasting, 32, 914–938,
https://doi.org/10.1016/j.ijforecast.2015.11.011, 2016.
Hyndman, R. J. and Khandakar, Y.: Automatic time series forecasting: the
forecast package for R, J. Stat. Softw., 27, 1–22,
https://doi.org/10.18637/jss.v027.i03, 2008.
Hyndman, R., Athanasopoulos, G., Bergmeir, C., Caceres, G., Chhay, L.,
O'Hara-Wild, M., Petropoulos, F., Razbash, S., Wang, E., Yasmeen, F., R Core
Team, Ihaka, R., Reid, D., Shaub, D., Tang, Y., and Zhou, Z.: forecast:
Forecasting functions for time series and linear models, R package version
8.3, available at:
https://cran.r-project.org/web/packages/forecast/index.html (last
access: 15 August 2018), 2018.
Koutsoyiannis, D., Yao, H., and Georgakakos, A.: Medium-range flow prediction
for the Nile: a comparison of stochastic and deterministic methods, Hydrolog.
Sci. J., 53, 142–164, https://doi.org/10.1623/hysj.53.1.142, 2008.
Newman, A. J., Sampson, K., Clark, M. P., Bock, A., Viger, R. J., and
Blodgett, D.: A large-sample watershed-scale hydrometeorological dataset for
the contiguous USA, UCAR/NCAR, Boulder, CO, https://doi.org/10.5065/D6MW2F4D, 2014.
Newman, A. J., Clark, M. P., Sampson, K., Wood, A., Hay, L. E., Bock, A.,
Viger, R. J., Blodgett, D., Brekke, L., Arnold, J. R., Hopson, T., and Duan,
Q.: Development of a large-sample watershed-scale hydrometeorological data
set for the contiguous USA: data set characteristics and assessment of
regional variability in hydrologic model performance, Hydrol. Earth Syst.
Sci., 19, 209–223, https://doi.org/10.5194/hess-19-209-2015, 2015.
Papacharalampous, G., Tyralis, H., and Koutsoyiannis, D.: One-step ahead
forecasting of geophysical processes within a purely statistical framework,
Geosci. Lett., 5, 12, https://doi.org/10.1186/s40562-018-0111-1, 2018b.
Papacharalampous, G., Tyralis, H., and Koutsoyiannis, D.: Predictability of
monthly temperature and precipitation using automatic time series forecasting
methods, Acta Geophys., 66, 807–831, https://doi.org/10.1007/s11600-018-0120-7, 2018c.
R Core Team: R: A language and environment for statistical computing, R
Foundation for Statistical Computing, Vienna, Austria, available at:
https://www.R-project.org/ (last access: 15 August 2018), 2018.
Silveira, C. S., Alexandre, A. M. B., Souza Filho, F. A., Junior, V., and
Cabral, S. L.: Monthly streamflow forecast for
National Interconnected System
(NIS) using Periodic Auto-regressive Endogenous Models (PAR) and Exogenous
(PARX) with climate information, RBRH, Porto Alegre, 22, e30,
https://doi.org/10.1590/2318-0331.011715186, 2017.
Spinu, V., Grolemund, G., and Wickham, H.: lubridate: Make Dealing with Dates
a Little Easier, R package version 1.7.4, available at:
https://CRAN.R-project.org/package=lubridate (last access:
15 August 2018), 2018.
Sun, A. Y., Wang, D., and Xu, X.: Monthly streamflow forecasting using
Gaussian Process Regression, J. Hydrol., 511, 72–81,
https://doi.org/10.1016/j.jhydrol.2014.01.023, 2014.
Taylor, S. J. and Letham, B.: prophet: Automatic Forecasting Procedure, R
package version 0.2, available at:
https://CRAN.R-project.org/package=prophet (last access:
15 August 2018), 2017.
Thornton, P. E., Thornton, M. M., Mayer, B. W., Wilhelmi, N., Wei, Y.,
Devarakonda, R., and Cook, R. B.: Daymet: Daily Surface Weather Data on a
1-km Grid for North America, Version 2, ORNL DAAC, Oak Ridge, Tennessee, USA,
https://doi.org/10.3334/ORNLDAAC/1219, 2014.
Tyralis, H.: HKprocess: Hurst-Kolmogorov Process, R package version 0.0-2,
available at:
https://CRAN.R-project.org/package=HKprocess (last
access: 15 August 2018), 2016.
Tyralis, H. and Koutsoyiannis, D.: Simultaneous estimation of the parameters
of the Hurst–Kolmogorov stochastic process, Stoch. Env. Res. Risk A., 25,
21–33, https://doi.org/10.1007/s00477-010-0408-x, 2011.
Tyralis, H. and Koutsoyiannis, D.: A Bayesian statistical model for deriving
the predictive distribution of hydroclimatic variables, Clim. Dynam., 42,
2867–2883, https://doi.org/10.1007/s00382-013-1804-y, 2014.
Tyralis, H., Dimitriadis, P., Koutsoyiannis, D., O'Connell, P. E., Tzouka,
K., and Iliopoulou, T.: On the long-range dependence properties of annual
precipitation using a global network of instrumental measurements, Adv. Water
Resour., 111, 301–318, https://doi.org/10.1016/j.advwatres.2017.11.010, 2018.
Warnes, G. R., Bolker, B., Gorjanc, G., Grothendieck, G., Korosec, A.,
Lumley, T., MacQueen, D., Magnusson, A., and Rogers, J.: gdata: Various R
Programming Tools for Data Manipulation, R package version 2.18.0, available
at:
https://CRAN.R-project.org/package=gdata (last access:
15 August 2018), 2017.
Wickham, H.: ggplot2, Springer International Publishing,
https://doi.org/10.1007/978-3-319-24277-4, 2016.
Wickham, H. and Chang, W.: ggplot2: Create Elegant Data Visualisations Using
the Grammar of Graphics, R package version 2.2.1, available at:
https://CRAN.R-project.org/package=ggplot2 (last access:
15 August 2018), 2016.
Wickham, H. and Chang, W.: devtools: Tools to Make Developing R Packages
Easier, R package version 1.13.4, available at:
https://CRAN.R-project.org/package=devtools (last access:
15 August 2018), 2018.
Wolpert, D. H.: The lack of a priori distinctions between learning
algorithms, Neural Comput., 8, 1341–1390, https://doi.org/10.1162/neco.1996.8.7.1341,
1996.
Xie, Y.: knitr: A Comprehensive Tool for Reproducible Research in R, in:
Implementing Reproducible Computational Research, Chapman and Hall/CRC, 2014.
Xie, Y.: Dynamic Documents with R and knitr, 2nd Edn., Chapman and Hall/CRC,
2015.
Xie, Y.: knitr: A General-Purpose Package for Dynamic Report Generation in R,
R package version 1.20, available at:
https://CRAN.R-project.org/package=knitr (last access: 15 August 2018), 2018.
Yang, T., Asanjan, A. A., Welles, E., Gao, X., Sorooshian, S., and Liu, X.:
Developing reservoir monthly inflow forecasts using artificial intelligence
and climate phenomenon information, Water Resour. Res., 53, 2786–2812,
https://doi.org/10.1002/2017WR020482, 2017.
Zeileis, A. and Grothendieck, G.: zoo: S3 infrastructure for regular and
irregular time series, J. Stat. Softw., 14, https://doi.org/10.18637/jss.v014.i06,
2005.
Zeileis, A., Grothendieck, G., and Ryan, J. A.: zoo: S3 Infrastructure for
Regular and Irregular Time Series (Z's Ordered Observations), R package
version 1.8-1, available at:
https://CRAN.R-project.org/package=zoo (last access:
15 August 2018), 2018.
