Regional Tourism Demand Forecasting With Machine Learning Models: Gaussian Process Regression vs. Neural Network Models in a Multiple-Input Multiple-Output Setting

SSRN Electronic Journal
doi 10.2139/ssrn.2945556
Full Text
Abstract

Available in full text

Date
Authors
Publisher

Elsevier BV

Related search

Combination Forecasts of Tourism Demand With Machine Learning Models

Applied Economics Letters
EconomicsEconometrics
2015English

Neural Network and Multiple Regression Models for PM10Prediction in Athens: A Comparative Assessment

Journal of the Air and Waste Management Association
ManagementMonitoringWaste ManagementDisposalAtmospheric SciencePolicyLawPollution
2003English

Artificial Neural Networks-Based Econometric Models for Tourism Demand Forecasting

2017English

Gaussian Process Regression With Multiple Response Variables

Chemometrics and Intelligent Laboratory Systems
Analytical ChemistryComputer Science ApplicationsProcess ChemistrySpectroscopyTechnologySoftware
2015English

A Multivariate Neural Network Approach to Tourism Demand Forecasting

SSRN Electronic Journal
2014English

An Input-Output Approach to Systems Described Using Multiple Models

IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications
2000English

Aggregation Problem in Multi-Regional Input-Output Models.

Studies in Regional Science
Social SciencesEnvironmental Science
1999English

Dividend Payout Forecast : Multiple Linear Regression vs Genetic Algorithm-Neural Network

CogITo Smart Journal
2019English

Online Sparse Multi-Output Gaussian Process Regression and Learning

IEEE Transactions on Signal and Information Processing over Networks
Computer NetworksInformation SystemsSignal ProcessingCommunications
2019English

Amanote Research

Note-taking for researchers

Follow Amanote

© 2024 Amaplex Software S.P.R.L. All rights reserved.

Privacy PolicyRefund Policy