Forecasting Mechanisms for Habitational Tourism

Tourism is globally accepted as one of the largest business industries that can easily be affected by a variety of external factors and events. Assessing and anticipating the impact of certain events on tourism has become a necessity in this increasingly dynamic and demanding world. According to TURIHAB, there are no mechanisms that allow objective forecasts for habitational tourism, namely its brands Solares de Portugal and Casas no Campo . This paper documents the techniques, models and platforms explored to develop the expected forecast mechanism. The arduous process of obtaining real data series to train the regression model, together with the exploration of two statistical models ( Random Forest and SARIMAX ) resulted in the tools for forecasting the TURIHAB association’s monthly occupancy rates for the years 2024 and 2025. In addition, adjustments on the training/test sets and hyperparameter tuning techniques were explored in order to improve the model. The performance evaluation followed specific metrics that fit the type of forecast expected. Finally, a comparison was made between the predicted and actual values as a validation strategy. The resulted dataset is in the process of being published. For any interest on accessing the developed models and used datatset, contact the authors.

Authors

Institutions

Publication Details

Journal
Procedia Computer Science
Published
2026-01-01
DOI
https://doi.org/10.1016/j.procs.2026.03.002
Primary Topic
Forecasting Techniques and Applications
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Forecasting Mechanisms for Habitational Tourism

Luís G.M. Ferreira, José P.R. Rocha, Maria C.S. Lima
Procedia Computer Science
Forecasting Techniques and Applications
article

Forecasting Mechanisms for Habitational Tourism

Luís G.M. Ferreira, José P.R. Rocha, Maria C.S. Lima
article en

Abstract

Tourism is globally accepted as one of the largest business industries that can easily be affected by a variety of external factors and events. Assessing and anticipating the impact of certain events on tourism has become a necessity in this increasingly dynamic and demanding world. According to TURIHAB, there are no mechanisms that allow objective forecasts for habitational tourism, namely its brands Solares de Portugal and Casas no Campo . This paper documents the techniques, models and platforms explored to develop the expected forecast mechanism. The arduous process of obtaining real data series to train the regression model, together with the exploration of two statistical models ( Random Forest and SARIMAX ) resulted in the tools for forecasting the TURIHAB association’s monthly occupancy rates for the years 2024 and 2025. In addition, adjustments on the training/test sets and hyperparameter tuning techniques were explored in order to improve the model. The performance evaluation followed specific metrics that fit the type of forecast expected. Finally, a comparison was made between the predicted and actual values as a validation strategy. The resulted dataset is in the process of being published. For any interest on accessing the developed models and used datatset, contact the authors.

Procedia Computer ScienceVol. 278
Universidade Politécnica do Cávado e do Ave (PT), University of Turabo (PR)
Decent work and economic growth
Openalex Percentile: Top 12%
Forecasting Techniques and Applications
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Forecasting Mechanisms for Habitational Tourism — Luís G.M. Ferreira, José P.R. Rocha, et al. · Procedia Computer Science (2026) | TGRS Research Map | TGRS