Prediction Model of National Visitors to the Moche Route in Peru based on Time Series and Neural Networks

Serquén O.; Alarcón R.; Bravo J.; Valdivia C.; Aquino J. · CEUR Workshop Proceedings (2023)

Resumen

The pandemic affected all economic sectors in the world, one of the most critical being the tourism sector, which is why the institutions involved need to manage urgent actions for its reactivation; innovation and digital transformation using disruptive technologies are important. The objective of the study is to use machine learning based on neural networks and time series to predict the influx of national visitors on the Moche Route of Peru, becoming a contribution to the use of artificial intelligence in favor of the social and economic development of the region. A methodology composed of 4 stages was developed: (1) data collection, (2) model analysis, (3) model development, and (4) model evaluation. Open access data was used during the period from January 2011 to December 2019, applying a recurring predictive process to determine the data in the pandemic years, using the algorithm based on time series and neural networks, finally, evaluated its operation and the proximity of the prediction to the real data. In conclusion, the model presents optimal results for all the tourist attractions of the Moche Route, demonstrating its prediction effectiveness, allowing the entities in charge of the tourism sector to have a tool for planning tourist itineraries and the necessary resources to cope to future demand.

Instituciones: Universidad Nacional Pedro Ruiz Gallo