Sales forecasting allows firms to plan their production outputs, which contributes to optimizing firms' inventory management via a cost reduction. However, not all firms have the same capacity to store all the necessary information through time. So, time-series with a short length are common within industries, and problems arise due to small time series does not fully capture sales' behavior. In this paper, we show the applicability of neural networks in a case where a company reports a short time-series given the changes in its warehouse structure. Given the neural networks independence form statistical assumptions, we use a multilayer-perceptron to get the sales forecasting of this enterprise. We find that learning rates variations do not significantly increase the computing time, and the validation fails with an error minor to five percent.

The promotion of academic programs, particularly at graduate levels, emerges as a response to market changes. In general, graduate programs are not a first order necessity which makes necessary the right promotion of such programs guarantee the attraction of prospective students, which enroll in some of them, which is essential for the financial sustainability of universities. Notably, the last one is a crucial problem for private universities. In this paper, we analyze the prospective students that enroll in a private to design better promotion strategies by using on data gathered by online sources. Specifically, we use clustering techniques to define marketing strategies based on segments of students. We find that age and city are crucial to promoting graduate programs while marital status and sex does not impact the decision of students in the university that we analyze.

The execution of smart contracts (SCs) relies on consensus algorithms that validate the miner who executes the contract and gets a fee to cover her expenditure. In this sense, miners are strategic agents who may focus on executing those contracts with the largest fee, to the detriment of other SCs’ execution times, which also harms the blockchain’s reputation. This paper analyzes the impact of miners’ competition on SCs’ execution times in a public blockchain. First, we explain that the Proof-of-Work mechanism casts similarities with a time auction, where the one who first adds blocks is the one who executes the contract and gets the fee. At equilibrium, costs negatively affect execution times, while the opposite holds concerning fees. However, this result does not capture the competition for other contracts; hence, we apply the Naïve Bayes method to classify SCs by considering a simulated database that comprises miners’ competition for several contracts. We observe that simultaneous competition generates patterns that differ from the ones expected by the auction solution. For example, miners’ valuation does not accelerate contracts’ execution, and high-cost smart contracts do not necessarily execute at last places.

Cryptocurrencies have recently emerged as financial assets that allow their users to execute transactions in a decentralized manner. Their popularity has led to the generation of huge amounts of data, specifically on social media networks such as Twitter. In this study, we propose an iterative kappa architecture that collects, processes, and temporarily stores data regarding transactions and tweets of two of the major cryptocurrencies according to their market capitalization: Bitcoin (BTC) and Ethereum (ETH). We applied a k-means clustering approach to group data according to their principal characteristics. Data are categorized into three groups: BTC typical data, ETH typical data, BTC and ETH atypical data. Findings show that activity on Twitter correlates to activity regarding the transactions of cryptocurrencies. It was also found that around 14% of data relate to extraordinary behaviors regarding cryptocurrencies. These data contain higher transaction volumes of both cryptocurrencies, and about 9.5% more social media publications in comparison with the rest of the data. The main advantages of the proposed architecture are its flexibility and its ability to relate data from various datasets.

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