Predicting tourism demands by google trends: A hidden markov models based study

Abstract

This article explores the usefulness of information from Google trends to predict the tourism demands. When a tourist interacts with the internet through a search engine, a website, or a social media platform, the traces of the interaction can be captured, stored, and analyzed. Research on most visited cities on the world showed how Amsterdam become a standout among the most visited city on the planet. Related works demonstrated that Google Trends have some values to predict the tourism industry, our new idea for this study is to utilize our model Hidden Markov Model (HMM) with Google Trends data and historical data from the electronic database Central Bureau Statistic (CBS) StatLine 2018 to predict the tourism demands in Amsterdam and to compare our method with existing methods. The search engine user needs to utilize search queries (keywords) identified with the tourism industry in Amsterdam will be extracted using application programming interface(API). We have trained and tested data by the Hidden Markov Model to predict next month tourists number in Amsterdam, For the two existing methods we tune their parameters to get the best results. Our experiments over real data from CBS StatLine demonstrate that our method not only outperforms the traditional and existing methods but also provides controllability to tourism prediction.