6,831 research outputs found
Modelling tourism demand to Spain with machine learning techniques. The impact of forecast horizon on model selection
This study assesses the influence of the forecast horizon on the forecasting performance of several machine learning techniques. We compare the fo recastaccuracy of Support Vector Regression (SVR) to Neural Network (NN) models, using a linear model as a benchmark. We focus on international tourism demand to all seventeen regions of Spain. The SVR with a Gaussian radial basis function kernel outperforms the rest of the models for the longest forecast
horizons. We also find that machine learning methods improve their
forecasting accuracy with respect to linear models as forecast horizons increase.
This results shows the suitability of SVR for medium and long term
forecasting.Peer ReviewedPostprint (published version
Evaluating probabilistic forecasts with scoringRules
Probabilistic forecasts in the form of probability distributions over future
events have become popular in several fields including meteorology, hydrology,
economics, and demography. In typical applications, many alternative
statistical models and data sources can be used to produce probabilistic
forecasts. Hence, evaluating and selecting among competing methods is an
important task. The scoringRules package for R provides functionality for
comparative evaluation of probabilistic models based on proper scoring rules,
covering a wide range of situations in applied work. This paper discusses
implementation and usage details, presents case studies from meteorology and
economics, and points to the relevant background literature
The analog data assimilation
In light of growing interest in data-driven methods for oceanic, atmospheric, and climate sciences, this work focuses on the field of data assimilation and presents the analog data assimilation (AnDA). The proposed framework produces a reconstruction of the system dynamics in a fully data-driven manner where no explicit knowledge of the dynamical model is required. Instead, a representative catalog of trajectories of the system is assumed to be available. Based on this catalog, the analog data assimilation combines the nonparametric sampling of the dynamics using analog forecasting methods with ensemble-based assimilation techniques. This study explores different analog forecasting strategies and derives both ensemble Kalman and particle filtering versions of the proposed analog data assimilation approach. Numerical experiments are examined for two chaotic dynamical systems: the Lorenz-63 and Lorenz-96 systems. The performance of the analog data assimilation is discussed with respect to classical model-driven assimilation. A Matlab toolbox and Python library of the AnDA are provided to help further research building upon the present findings.Fil: Lguensat, Redouane. UniversitĂ© Bretagne Loire; FranciaFil: Tandeo, Pierre. UniversitĂ© Bretagne Loire; FranciaFil: Ailliot, Pierre. University of Western Brittany. Laboratoire de MathĂ©matiques de Bretagne Atlantique; FranciaFil: Pulido, Manuel Arturo. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas. Centro CientĂfico TecnolĂłgico Conicet - Nordeste. Instituto de Modelado e InnovaciĂłn TecnolĂłgica. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas Naturales y Agrimensura. Instituto de Modelado e InnovaciĂłn TecnolĂłgica; ArgentinaFil: Fablet, Ronan. UniversitĂ© Bretagne Loire; Franci
- …