Skip to main content

Time Series Features and Machine Learning Forecasts

Buy Article:

$30.00 + tax (Refund Policy)

In this study we combine the results of two independent analyses to position Spanish regions according to both the characteristics of the time series of international tourist arrivals and the accuracy of predictions of arrivals at the regional level. We apply a seasonal trend decomposition procedure based on nonparametric regression to isolate the different components of the series and calculate the main time series features. Predictions are generated with several machine learning models in a recursive multistep-ahead forecasting experiment. Finally, we summarize all the information from the two previous experiments using categorical principal component analysis. By overlapping the distribution of the regions and the component loadings of each variable along both dimensions, we observe that entropy and dispersion show an inverse relation with forecast accuracy, but the interactions between the rest of the features and accuracy are heavily dependent on the forecast horizon. On this evidence, we conclude that in order to increase forecast accuracy of tourist arrivals at the regional level, model selection should be region specific and based on the forecast horizon.


Document Type: Research Article

Publication date: December 7, 2020

This article was made available online on September 16, 2020 as a Fast Track article with title: "Time series features and machine learning forecasts".

More about this publication?
  • Established in 1996, Tourism Analysis is an interdisciplinary journal that provides a platform for exchanging ideas and research in tourism and related fields. The journal aims to publish articles that explore a broad range of research subjects, including, but not limited to, the social, economic, cultural, environmental, and psychological aspects of tourism, consumer behavior in tourism, sustainable and responsible tourism, and effective operations, marketing, and management.

    Tourism Analysis focuses on both theoretical and applied research and strives to promote innovative approaches to understanding the complex and dynamic nature of tourism, its stakeholders, businesses, and its effects on society. The journal welcomes articles on innovative research topics and methodologies beyond the traditional theory-testing sciences, such as robotics, computational sciences, and data analytics.

    Our primary goal is to contribute to the development and advancement of new knowledge in tourism while fostering critical reflections and debates on the radical changes and evolution in tourism among scholars, practitioners, policymakers, and other stakeholders.
  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content