Forecasting occupancy in hospitality business with autoregressive time-series models does not intercept occasional impact of public events. Our goal was to find appropriate datasets and enrich existing predictive models to account for rare and explicable demand surges. The paper proposes processing framework: data source types and formats, and forecast algorithms based on natural language processing. The study shows that classical models using word collocations outperform state of the art deep neural networks. Also, the collocations that turn out to be important, occupy certain locations in a graph that represents the natural language. The findings may result in yet improved forecasts, leading to smarter offer pricing and, finally, increased competitiveness in hospitality business. They may also serve public interest in areas like parking management or public transport planning. INDEX TERMS Time series analysis, recurrent neural networks, regression analysis, natural language processing, predictive models.
Conway's Law states that in successful software projects, the organizational structure of programmer teams corresponds to the architecture of the developed system. It means that, ideally, each developer team works on its software module, and only on that module. We propose an approach to assess the difference between code structure and organizational structure. It is based on agglomerative clustering of modules and developers, followed by the search for best possible mapping between the groups. We applied the approach to a number of popular open source projects. The results show that these projects hardly obey Conway's law, due to the scale-free nature of both types of deduced networks, i.e., of software modules and the developers.
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