English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

This study assesses the influence of the forecast horizon on the forecastingperformance 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 demandto all seventeen regions of Spain. The SVR with a Gaussian radial basisfunction kernel outperforms the rest of the models for the longest forecasthorizons. We also find that machine learning methods improve their forecastingaccuracy with respect to linear models as forecast horizons increase. Thisresult shows the suitability of SVR for medium and long term forecasting.

Modelling tourism demand to Spain with machine learning techniques. The impact of forecast horizon on model selection