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As the digitalization process has become more and more important in our daily lives, during recent decades e-commerce has greatly increased in popularity, becoming increasingly used, therefore representing an extremely convenient alternative to traditional stores. In order to develop and maintain profitable businesses, traders need accurate forecasts concerning their future sales, a very difficult task considering that these are influenced by a wide variety of factors. This paper proposes a novel e-commerce sales forecasting method that dynamically builds a Directed Acyclic Graph Neural Network (DAGNN) for Deep Learning architecture. This will allow for long-term, fine-grained forecasts of daily sales revenue, refined up to the level of product categories. The developed forecasting method provides the e-commerce store owner an accurate forecasting tool for predicting the sales of each category of products for up to three months ahead. The method offers a high degree of scalability and generalization capability due to the dynamically incremental way in which the constituent elements of the DAGNN’s architecture are obtained. In addition, the proposed method achieves an efficient use of data by combining the numerous advantages of its constituent layers, registering very good performance metrics and processing times. The proposed method can be generalized and applied to forecast the sales for up to three months ahead in the case of other e-commerce stores, including large e-commerce businesses.

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Increasing road traffic safety is a requirement for the growth and development of economies and society in general, and any endeavour contributing to an increase in the safety of road traffic participants must be evaluated and even encouraged, as there is no measurement for the value of human life. An important technological improvement that contributes to road traffic safety by reducing response time in case of an accident is the eCall In Vehicle System, which provides real-time data on-site, even without human intervention. The aim of the present study is to approach the introduction of the eCall In Vehicle System from a cost-benefit standpoint of equipping aftermarket passenger vehicles with this technology. To this end, we propose an analytic research approach in the form of a conclusive study on statistical data, in order to support decisions regarding future implementation.

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