Predictive capability of Google search in forecasting tourist arrival in the Philippines
| dc.contributor.advisor | Debuque-Gonzales, Margarita | |
| dc.contributor.author | Ruiz, John Matthew D. | |
| dc.contributor.author | Zacarias, Ysabelle T. | |
| dc.date.accessioned | 2024-09-30T08:14:11Z | |
| dc.date.available | 2024-09-30T08:14:11Z | |
| dc.date.issued | 2017-12-22 | |
| dc.description.abstract | Google Trends, a new source of data, shows the index of worldwide search volume queries in the internet. This study took advantage of this open data to look into its potentiality in improving the tourist arrival forecast for the Philippines using fifty keywords, which were divided into three categories: tourism, political and economic, and safety and order. A 12-month tourism forecast horizon were made for the top five largest sources of tourists for the Philippines, including the OFWs, using three models. A multivariate Vector Autoregression (VAR) model with Google data and two benchmark models: Autoregressive Integrated Moving Average (ARIMA) and a VAR model without Google data were compared using RMSE and MAPE criteria. This study provided evidence that the VAR model with Google data generally performed better in terms of RMSE and MAPE criteria as compared to the benchmark models. The set of keywords under the tourism category also proved to be the most significant in predicting tourist arrivals. | |
| dc.identifier.uri | https://selib.upd.edu.ph/etdir/handle/123456789/511 | |
| dc.language.iso | en | |
| dc.subject | Google trends | |
| dc.subject | explanatory variables | |
| dc.subject | Vector Autoregression Model (VAR) | |
| dc.subject | Autoregressive Integrated Moving Average (ARIMA) | |
| dc.subject | Root Mean Square Error (RMSE) | |
| dc.subject | Moving Average Percentage Error (MAPE) | |
| dc.title | Predictive capability of Google search in forecasting tourist arrival in the Philippines | |
| dc.type | Thesis |