Abstract

Abstract
Several developments have been observed recently in areas such as Web development, social networks, interface design, recommendation systems and Geographic Information Systems (GIS). The integration of these developments can provide a superior experience, greater than the sum of their individual contributions, regarding user satisfaction. This paper proposes an integration of all these innovations in e-tourism, more specifically by the development of a Web based geographic information platform adaptable to any tourist region. As a case study, we also show how this platform was adapted to the Douro region, in Portugal. The Web platform developed as a proof of concept combines geospatial information from diverse and heterogeneous data sources, encompassing events, news, routes and points of interest (POI). This platform provides also a recommendation engine and features the possibility that users can contribute with content as part of the community, thus emerging a mini social network. © 2011 AISTI.

Abstract
Retailers must have accurate sales forecasts to efficiently and effectively operate their businesses and remain competitive in the marketplace. Global forecasting models like RNNs can be a powerful tool for forecasting in retail settings, where multiple time series are often interrelated and influenced by a variety of external factors. By including covariates in a forecasting model, we can often better capture the various factors that can influence sales in a retail setting. This can help improve the accuracy of our forecasts and enable better decision making for inventory management, purchasing, and other operational decisions. In this study, we investigate how the accuracy of global forecasting models is affected by the inclusion of different potential demand covariates. To ensure the significance of the study's findings, we used the M5 forecasting competition's openly accessible and well-established dataset. The results obtained from DeepAR models trained on different combinations of features indicate that the inclusion of time-, event-, and ID-related features consistently enhances the forecast accuracy. The optimal performance is attained when all these covariates are employed together, leading to a 1.8% improvement in RMSSE and a 6.5% improvement in MASE compared to the baseline model without features. It is noteworthy that all DeepAR models, both with and without covariates, exhibit a significantly superior forecasting performance in comparison to the seasonal naive benchmark.