## 2 Recurrent Neural Networks and ARIMA Models for Euro/Dollar Exchange Rate Forecasting [3]  
For governments, financial institutions, and investors, understanding how the exchange rate will behave over the short, medium and long term is crucial since this information reflects the country's economic health. As a consequence, this has relevant implicationsfor estimating the cost of goods in international trade. In this paper, [PERSON], [PERSON] and [PERSON] [3] propose an in-depth and comparative analysis of different forecast models to assess short/long term accuracy using EUR/USD exchange rate data. In particular, three methods were studied: Autoregressive Integrated Moving Average (ARIMA), Elman-type recurrent neural network (RNN) and short-long-term memory (LSTM). To carry out this work, the evolution of the daily exchange rate was considered during a period of 21 years (1998-2019) using different window intervals. The average accuracy for LSTM in the 22-day window was 71.76%, which satisfactorily compared with previous studies. The authors conclude that LSTM fits better in the short term (up to a maximum accuracy of 95.99% in some observations). On the other hand, Elman networks turned out to be more suitable for long-term analysis. It should also be noted that LSTM networks offered an advantage over Elman networks in capturing complex patterns and non-linearities in the data, and therefore should be incorporated in future analyses with all types of windows. For this reason, the authors suggested as next steps the study and development of combined and hybrid models, which may improve the results presented here. A Big Data Approach to Customer Relationship Management Strategy in Hospitality Using Multiple Correspondence Domain Description [4]  
Previous studies have highlighted the effectiveness of simple Big Data tools, such as Bootstrap resampling techniques, in determining client profiles. However, these tools lack the ability to provide managers with information about the relationships between various features and categories (cross-relationships). This study [4] was conducted following a three-step analysis method. First, Multiple Correspondence Analysis (MCA) described the interactions between categories and features. Second, bootstrap resampling provided insights into the statistical variability of the feature maps. Finally, kernel methods created visually interpretable domain descriptions based on confidence areas in the maps. The use of bootstrap resampling and confidence intervals allows us to enhance the interpretability and statistical robustness of the results. The methodology was applied to analyze the profiles of clients in an international hotel chain, focusing on nationality and differentiating between repeaters and first-timers. The results showed differences in expenditure and age among nationalities and highlighted the importance of marital status in the repeater profile. The study emphasized the significance of nationality in explaining expenditure variations and suggested strategies for targeting high-value segments, such as Spanish repeaters. This methodology offers a statistically robust approach to examining CRM profiles in the hospitality sector, enabling effective decision-making. On the Differential Analysis of Enterprise Valuation Methods as a Guideline for Unlisted Companies Assessment (I): Empowering Discounted Cash Flow Valuation [5]  
The two-fold work presented by [PERSON] et al. [5,6] brings into perspective the practical interest application-rooted _On the Differential Analysis of Enterprise Valuation Methods as a Guideline for Unlisted Companies Assessment_. A comprehensive analysis is carried out in two steps. In Part (I), _Empowering Discounted Cash Flow Valuations_[5], the technical details of the problem are presented widely to the reader, as well as the relevance of company valuation today. Data analysis is carried out on sets of selected companies in different fields, comprising conventional linear regression techniques.