=Paper=
{{Paper
|id=Vol-3858/paper2
|storemode=property
|title=Prediction Model of National Visitors to the Moche Route in Peru based on Time Series and Neural Networks
|pdfUrl=https://ceur-ws.org/Vol-3858/paper2.pdf
|volume=Vol-3858
|authors=Oscar Serquén,Roger Alarcón,Jessie Bravo,Carlos Valdivia,Janet Aquino
|dblpUrl=https://dblp.org/rec/conf/ithgc/SerquenAJVL23
}}
==Prediction Model of National Visitors to the Moche Route in Peru based on Time Series and Neural Networks==
https://ceur-ws.org/Vol-3858/paper2.pdf
Prediction Model of National Visitors to the Moche Route
in Peru based on Time Series and Neural Networks
Oscar Serquén1, Roger Alarcón2, Jessie Bravo3, Carlos Valdivia4, Janet Aquino5
1 2 3 4 5Digital Transformation Research Group, Pedro Ruiz Gallo National University, Juan XXIII 395, Lambayeque, Perú
Abstract
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.
Keywords
Time series, neural networks, prediction, machine learning, tourism 1
1. Introduction
The COVID-19 pandemic had an unprecedented impact on the tourism industry worldwide,
paralyzing travel and considerably reducing tourist arrivals. The reactivation of tourism today is
a very important issue for countries and their authorities who must adequately manage tourism
demand and volume. This reactivation is using information technology, changing traditional
tourism and allowing the information-based tourism industry to develop, generating innovation
[1], likewise, [2] highlights the growing use of new technologies and artificial intelligence (AI)
techniques in the tourism sector that allows improvement in speed, creativity and knowledge of
the service, in the search of improving tourist satisfaction.
In Peru, tourism activity by 2023 will contribute 2.5% to the National GDP [3], projecting the
arrival of 2.2 million foreign visitors and 34.3 million trips by domestic tourists. The income
generated by domestic tourism from 2012 to 2022 still does not achieve the income achieved
before the pandemic [4].
One of the most visited tourist routes is the Moche Route that integrates the regions of La
Libertad and Lambayeque, presenting archaeological, natural, cultural and landscape attractions
[5]. In these regions of the northern coast of Peru, some of the most important pre-Columbian
civilizations: Moche, Chimú and Sicán, being promoted by the National Strategic Tourism Plan
2025 [6].
Similarly, the Peruvian state, in July 2023, enacted Law No. 31814 which promotes the use of
AI within the framework of the national digital transformation process with the purpose of
promoting the economic and social development of the country [7]. An important advance for AI
ITHGC 2023: IV International Tourism, Hospitality & Gastronomy Congress, October 25–27, 2023, Lima, Peru
aserquen@unprg.edu.pe (O. Serquén); ralarcong@unprg.edu.pe (R. Alarcón); jbravo@unprg.edu.pe (J. Bravo);
cvaldivias@unprg.edu.pe (C. Valdivia); jaquino@unprg.edu.pe (J. Aquino)
0000-0001-9968-493X (O. Serquén); 0000-0002-2895-9120 (R. Alarcón); 0000-0001-6841-2536 (J. Bravo);
0000-0002-2895-9120 (C. Valdivia); 0000-0003-0536-3882 (J. Aquino)
© 2023 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
CEUR
ht
tp:
//
ceur
-ws
.or
g
Works
hop I
SSN1613-
0073
Pr
oceedi
ngs
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
is open data, where, according to the United Nations Electronic Government Report 2022, Peru is
located in first place in Latin America [8].
1.1. Tourism and digital transformation
In the era of digital transformation, AI has revolutionized numerous sectors, so [9, 10] point
out that forecasting future trends is of utmost importance for managers and decision makers in
different sectors and specifically tourism has been no exception. The complex characteristics of
tourist arrival series, such as seasonality, randomness and non-linearity, make tourist arrival
forecasting still a difficult task [11].
1.2. Predictive models in tourism
Predictive models are components of AI that serve as an analytical tool that uses historical
data to predict future values. According to [12] there are several models for the prediction of
tourists or visitors, among them: Random Forest (RF), neural networks (RN) and support vector
machines (SVM), however, there is no single technique or algorithm that delivers consistent
forecasts, but the results depend on the model and technique applied, the number of observations
and the characteristics of the dataset. In research by [13], he notes that RNs have been shown to
be particularly accurate in univariate time series forecasting environments.
In the study [14] it is analyzed and managed to predict tourism demand in Vietnam using a
multilayer perceptron artificial RN prediction model. In the case of data during the COVID-19
pandemic, dummy variables were used to model the impact of the pandemic and the number of
tourists expected. Other techniques are also used, as stated [15], through the analysis of historical
tourism volume and convolutional neural network models, which also allowed estimating
tourism demand during the COVID-19 pandemic.
Taking into account the above, the main objective of the research is to use machine learning
based on neural networks and time series to predict the influx of national visitors in the Moche
Region of Peru, constituting a contribution to the use of artificial intelligence in favor of the social
and economic development of the country.
2. Materials and methods
Figure 1 describes the methodology applied in this research, which consists of four main stages:
(1) data collection, (2) model analysis, (3) model development, and (4) model evaluation. The
Python version 3 programming language and Google Colab storage were used for processing.
Figure 1: Applied Research Methodology
2.1. Data collection
The data used in the proposed model were divided into two dimensions, as described in Table
1, during the period between January 2011 and December 2019. The years from 2020 to 2022,
the period of the COVID-19 pandemic, no data are recorded, so their treatment is explained in the
development of the proposed model.
Table 1
Variables used in the prediction of the model.
Dimension Variable Description Type
Temporal DATE Year and Month of visit Numerical
Arrival of National TOTAL_NATIONAL Number of domestic Numerical
Tourists tourists visiting tourist
attractions on a
monthly basis
The time dimension represents the year and month within the evaluated period and the
dimension Arrival of National Tourists are data extracted from the Internet [16], from the Open
Data Platform of the Government of Peru, which expresses the number of national tourists who
visited the tourist attractions of the Moche Route in Peru on a monthly basis. The tourist
attractions of the Moche Route are located in the departments of La Libertad and Lambayeque, in
the northern part of Peru, which are detailed in Table 1.
Table 2
Tourist attractions on the Moche Route.
Location Tourist Attraction Type
La Libertad Huaca Arco Iris Archaeological Complex Archaeological site
La Libertad Huaca del Sol y la Luna Archaeological Complex Archaeological site
La Libertad Huaca el Brujo Archaeological Complex Archaeological site
La Libertad Chan Chan Site Museum Museum
Lambayeque Brüning National Archaeological Museum
Lambayeque Huaca Chotuna Site Museum - Chornancap Museum
Lambayeque Huaca Rajada Site Museum – Sipan Museum
Lambayeque Tucume Site Museum Museum
Lambayeque Sican National Museum Museum
Lambayeque Royal Tombs of Sipan Museum Museum
Lambayeque Pomac Forest Historical Sanctuary Museum
2.2. Model Analysis
Preprocessing was carried out, for which the data was standardized by applying a scale with
a range between -1 and 1, which allows the data to be scaled, without losing the originals, then
the transformation of the same from rows to columns of 36 elements and 72 rows, which will
serve as input data to the model.
The choice of 36 input elements provided better results when the model was applied, with
respect to larger or smaller quantities. The pre-processed data maintained the time series
structure, for which they were transformed into data required for the application of the selected
algorithm.
The prediction algorithm used is time-series neural networks, which is a method of artificial
intelligence that teaches computers to process data in a similar way to the human brain.
The type of neural network applied is feedforward (unidirectional or forward-propagating
networks), made up of three layers, a dense layer, a flatten layer and a dense layer. In addition,
the hyperbolic tangent activation function, applied in the dense layers, was used [17].
2.3. Model Development
The development of the model begins in the data division stage, selecting from the 72 existing
rows, 60 rows for the training stage and 12 rows for model validation. The algorithm makes block
predictions of 12-month national visitors, for which it takes input information from the previous
36 months. This process is repeated recurrently, using as data the information collected from
2011 to 2019 to predict the year 2020, subsequently the predicted data from 2020 was added as
input data to the model and the year 2021 was predicted, the results obtained again served as
input to the model, having aggregate data from the year 2020 and 2021, for the year 2022 the
same process was carried out and the prediction for the year 2023 was completed.
2.4. Evaluation of the model
The evaluation of the model was based on the metrics: mean absolute error (MAE) and the
mean square error (MSE). MAE is defined as a loss function calculated from the sum of the
absolute differences between the expected value and the predicted variables and MSE measures
the average of the squared errors, that is, the difference between the estimated value and the
predicted value, the which were useful to examine the accuracy of the prediction model. In both
metrics, the lower the calculated value, the better the prediction model obtained will be.
3. Results and Discussion
The neural network model, as seen in Figure 2, has an input layer of 36 neurons formed by the
number of monthly visitors in blocks of 36 months, whose data was scaled in the data preparation
process. As part of the neural network, a hidden layer is included, which allows prediction
functions to be executed to generate an output layer, which corresponds to the prediction of
month 37, still in its scaled form. This process is repeated taking the generated output as a new
input to the neural network.
Figure 2: Developed neural network model
For the evaluation, a graphical and numerical comparison was made between the real data and
the data predicted by the model, which correspond to the entire Moche Route separated by
regions.
Figure 3 shows the comparison between the years 2019 and 2022, of the 4 tourist attractions
that correspond to the La Libertad region. It can be seen that the predicted data follow the trend
and adjust to the actual data, it is also worth mentioning that as the year prediction progresses, a
substantial improvement is observed between the prediction and the actual data.
Figure 3: Comparison of Actual Values with Predicted Values - La Libertad.
Figures 4 and 5 show the comparison of the actual and predicted data for the 7 tourist
attractions that correspond to the Lambayeque region, in which 4 attractions present values that
fit very well with the data using a neural network model, while in 3 tourist attractions the
predicted data vary significantly from the actual data, so it can be indicated that the model does
not fit these values as well.
2019 2020 2021 2022
Figure 4: Comparison of Actual Values with Predicted Values - Lambayeque.
2019 2020 2021 2022
Figure 5: Comparison of Actual Values with Predicted Values - Lambayeque.
The MAE and MSE metrics were the performance measures used to evaluate the results
obtained from the 11 different locations corresponding to the Moche Route.
Small values in the residual statistics, MAE and MSE, reflect better goodness-of-fit, i.e., values
close to zero are considered a good prediction.
The accuracy of the predictions generated by the model was evaluated using a training set and
a test set. A 5-year {Xt} training set (estimation period) and 4-year {Xt} test data (validation
period) were considered and used for month-to-month prediction.
Table 3 presents the results of the evaluation of the model based on the metrics for each of the
different tourist attractions, observing the results it is found that in the year 2020 there are 3
tourist attractions out of the 11 that have very high values (highlighted in bold) in the MSE metric
(Huaca el Brujo, Tucume Site Museum and Huaca Chotuna Site Museum - Chornancap) which
could indicate a lower than expected forecast. For the year 2021, it is reduced to 2 tourist
attractions with high values in this metric, also indicating a forecast below expectations. But in
2022 and 2023, all attractions have small values, indicating a better fit of the prediction in the
model.
Finally, in the analysis of the year 2023, it can be seen that the Huaca del Sol y de la Luna,
Brüning National Archaeological Museum and the Huaca Arco Iris present the lowest results,
which indicates that the data of these attractions fit very well with the predictions made by the
model. The rest of the tourist attractions also present a good prediction. Similar results can be
seen for the MAE metric.
Table 3
Evaluation results using MSE and MAE as metrics.
MSE MAE
Tourist Attraction 2020 2021 2022 2023 2020 2021 2022 2023
Huaca Arco Iris
Archaeological 0.0573 0.0493 0.0411 0.0400 0.1775 0.1650 0.1550 0.1465
Complex
Huaca el Brujo
Archaeological 0.1039 0.0697 0.0527 0.0586 0.2160 0.1987 0.1547 0.1788
Complex
Huaca del Sol y la
Luna
0.0620 0.0629 0.0479 0.0287 0.1863 0.1775 0.1534 0.1182
Archaeological
Complex
Chan Chan Site
0.0928 0.1093 0.0742 0.0702 0.2149 0.2347 0.1882 0.1939
Museum
Brüning National
0.0982 0.0809 0.0515 0.0395 0.2251 0.1899 0.1605 0.1454
Archaeological
Royal Tombs of
0.0503 0.0504 0.0492 0.0410 0.1568 0.1529 0.1574 0.1453
Sipan Museum
Sican National
0.0889 0.0888 0.0951 0.0908 0.1804 0.1890 0.1820 0.2082
Museum
Tucume Site
0.1017 0.0748 0.0745 0.0582 0.2355 0.1973 0.1853 0.1749
Museum
Huaca Rajada Site
0.0465 0.0487 0.0537 0.0488 0.1596 0.1578 0.1529 0.1506
Museum – Sipan
Huaca Chotuna Site
Museum - 0.1345 0.2172 0.0955 0.0921 0.2280 0.3243 0.2115 0.1922
Chornancap
Pomac Forest
Historical 0.0680 0.0756 0.0673 0.0576 0.2019 0.1976 0.1869 0.1660
Sanctuary
According to the results of the forecasts of the 11 tourist attractions, the two tourist attractions
with the best prediction results are described below.
In Figure 6, you can see the forecast of national visitors to the tourist attraction Brüning
National Archaeological Museum, identifying that the trend of visitors is maintained over the
years, the behavior of visitors between the months of September to November increases, while
from December to June it has a tendency to decrease.
Figure 6: Forecast of national visitors at the Brüning National Archaeological Museum.
Figure 7 shows the forecast of national visitors to the Huaca del Sol y de la Luna archeological
site, showing that the trend of visitors is maintained between 2021 and 2023. In addition, it can
be seen that the months of July and August maintain high values for all years, while the months
of April and May have the lowest values.
Figure 7: Forecast of national visitors in the Huaca del Sol y de la Luna Complex.
The results of the predictions made thanks to the application of computer science in tourism,
through artificial intelligence, allow to understand the large sets of tourism data, analyze them
through machine learning techniques to identify patterns and future trends in the prediction and
management of tourism demand, allowing the optimal allocation of resources, improving the
experience of tourists and contributing to more informed decision making at the business and
government level.
4. Conclusions
This study presents a predictive model, based on neural networks and time series to determine
the prediction of visitors to the Moche Route in the years 2020 to 2023, applying a recurrent
predictive process.
In the recurring predictive process, the MSE and MAE metrics improved their results in each
year evaluated, identifying in 2020 only three tourist attractions: Huaca el Brujo, Túcume Site
Museum and Huaca Chotuna - Chornancap Site Museum with prediction below expectations; On
the other hand, by 2021 it was reduced to two tourist attractions and finally, For the years 2022
and 2023, all the attractions present small values in the metrics, indicating a better fit of the
prediction in the applied model.
The prediction model reached optimal values with the MAE and MSE evaluation metrics, which
achieves significant results of the expected demand of national tourists for the tourist attractions
of the Moche Route.
This research is framed as a contribution to Law No. 31814 on the use of Artificial Intelligence
in the Peruvian state, since it will allow the entities in charge of the tourism sector to have a tool
for the planning of tourist itineraries and the necessary resources to face the future demand.
References
[1] Kong, Y.: Real-time processing system and Internet of Things application in the cultural
tourism industry development. Soft Computing. 27, 10347–10357 (2023).
https://doi.org/10.1007/s00500-023-08304-8
[2] Dangwal, A., Kukreti, M., Angurala, M., Sarangal, R., Mehta, M., Chauhan, P.: A Review on
the Role of Artificial Intelligence in Tourism. Presentado en Proceedings of the 17th
INDIACom; 2023 10th International Conference on Computing for Sustainable Global
Development, INDIACom 2023 (2023)
[3] El Peruano: Titular del Mincetur: Actividad turística contribuirá este año con 2.5% al PBI
nacional [Entrevista], https://elperuano.pe/noticia/214558-titular-del-mincetur-
actividad-turistica-contribuira-este-ano-con-25-al-pbi-nacional-entrevista
[4] SIT-MINCETUR: Sistema de Inteligencia Turística,
https://www.mincetur.gob.pe/centro_de_Informacion/mapa_interactivo/index.html
[5] Bravo, J., Alarcón, R., Valdivia, C., Serquén, O.: Application of Machine Learning Techniques
to Predict Visitors to the Tourist Attractions of the Moche Route in Peru. Sustainability
(Switzerland). 15, (2023). https://doi.org/10.3390/su15118967
[6] PENTUR: Plan Estratégico Nacional de Turismo del Perú-PENTUR,
https://www.gob.pe/institucion/mincetur/informes-publicaciones/22123-plan-
estrategico-nacional-de-turismo-del-peru-pentur.
[7] El Peruano: Ley que promueve el uso de la Inteligencia Artificial en favor del desarrollo
económico y social del país, http://busquedas.elperuano.pe/dispositivo/NL/2192926-1.
[8] Vilches, C.: Biblioguias: Desde el gobierno digital hacia un gobierno inteligente: UN E-
Government Survey, https://biblioguias.cepal.org/gobierno-digital/un-egovernment-
survey.
[9] Salehi, S.: Employing a Time Series Forecasting Model for Tourism Demand Using ANFIS.
Journal of Information and Organizational Sciences. 46, 157–172 (2022).
https://doi.org/10.31341/jios.46.1.9.
[10] Kirtil, İ.G., Aşkun, V.: Artificial Intelligence in Tourism: A Review and Bibliometrics
Research. Advances in Hospitality and Tourism Research (AHTR). 9, 205–233 (2021).
https://doi.org/10.30519/ahtr.801690.
[11] Liang, X., Wu, Z.: Forecasting tourist arrivals using dual decomposition strategy
and an improved fuzzy time series method. Neural Comput & Applic. 35, 7161–7183
(2023). https://doi.org/10.1007/s00521-021-06671-7.
[12] De Jesus, N.M., Samonte, B.R.: AI in Tourism: Leveraging Machine Learning in
Predicting Tourist Arrivals in Philippines using Artificial Neural Network. International
Journal of Advanced Computer Science and Applications. 14, 816–823 (2023).
https://doi.org/10.14569/IJACSA.2023.0140393.
[13] Semenoglou, A.-A., Spiliotis, E., Assimakopoulos, V.: Data augmentation for
univariate time series forecasting with neural networks. Pattern Recognition. 134,
(2023). https://doi.org/10.1016/j.patcog.2022.109132.
[14] Nguyen, L.Q., Fernandes, P.O., Teixeira, J.P.: Analyzing and Forecasting Tourism
Demand in Vietnam with Artificial Neural Networks. Forecasting. 4, 36–50 (2022).
https://doi.org/10.3390/forecast4010003.
[15] Wu, B., Wang, L., Zeng, Y.-R.: Interpretable tourism demand forecasting with
temporal fusion transformers amid COVID-19. Applied Intelligence. 53, 14493–14514
(2023). https://doi.org/10.1007/s10489-022-04254-0.
[16] datosTurismo,
http://datosturismo.mincetur.gob.pe/appdatosTurismo/Content1.html.
[17] Rivas-Asanza, W., Mazon-Olivo, B., Mejia, F.: Capítulo 1: Generalidades de las redes
neuronales artificiales. Presentado en junio 29 (2018).