Tourism and Big Data: Forecasting with Hierarchical and Sequential Cluster Analysis

Abstract

A new Big Data cluster method was developed to forecast the hotel accommodation market. The simulation and training of time series data are from January 2008 to December 2019 for the Spanish case. Applying the Hierarchical and Sequential Clustering Analysis method represents an improvement in forecasting modelling of the Big Data literature. The model is presented to obtain better explanatory and forecasting capacity than models used by Google data sources. Furthermore, the model allows knowledge of the tourists’ search on the internet profiles before their hotel reservation. With the information obtained, stakeholders can make decisions efficiently. The Matrix U1 Theil was used to establish a dynamic forecasting comparison.

Bernoulli Time Series Modelling with Application to Accommodation Tourism Demand

“…This method of estimation, based on a matrix of instruments, allows obtaining consistency properties of the estimated parameters. The results of our forecasting model improve the data of models contrasted in the tourism forecasting literature such as the Entropy model [8], Seasonal Autoregressive Integrated Moving Average (SARIMA) [9] and Autoregressive Distributed Lags extended to Seasonality (ARDL + Seasonality) [10]. The results of Ratio Theil's (RT s U 1 ) verify these empirical results.…”

“…Forecasting tasks will be compared with automatic TRAMO-SEATS for SARIMA models [26] and causality models such as Autoregressive Distributed Lags Extended to Seasonality, in addition to the causality model with Entropy factor [27]. For the evaluation of the prediction, we propose the Root Mean Squared Error (RMSE) criterion and the relative dimensionless criterion of RT s U 1 [10]. In the following paragraphs, we will describe the application methodology in the empirical section.…”

“…On the other hand, we will propose the relative criterion RT s U 1 , which is designed to perform model comparisons for prediction periods with the time horizon h. The benchmark is based on the concept of inequality of Theil U 1 [32] and developed for forecastings comparisons between the results of modeling [10]:…”