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Item type:Publication, Predictive analytics to determine the Bitcoin price rise using machine learning techniques(Seventh Sense Research Group, 2022-03-01)As we know, today there have been several variations in the price of Bitcoin as it is not stable, that is to say, the price of Bitcoin rises and falls exaggeratedly as there are several factors that focus on the opening and closing price values of Bitcoin, also the highest and lowest Bitcoin price values reached in a day. Bitcoin price is determined by supply and demand. If user demand for this cryptocurrency is high, the price will increase and if it is very low, will go to. The price of Bitcoin has fluctuated considerably in recent years. This means that it is constantly changing and is in a stable range without major changes. In this paper, a machine learning method, also called Machine Learning, is proposed, which will allow us to automatically search and interpret relevant information from a large amount of data. Machine learning is the branch of artificial intelligence science that creates automated learning systems. In the case study, the Recurrent Neural Network (LSTM) algorithm will be used for the predictive analysis of Bitcoin value. Recurrent Neural Networks are network layers for analyzing time series and time-series data. An LSTM recurrent neural network aims to learn long-term dependencies; that is to say, learn the dependencies of future values of a sequence on previous values. The results of this research work showed that, by applying the Machine Learning technique and the LSTM algorithm, it was possible to predict the Bitcoin price increase. These results could benefit different companies or financial institutions in the investment of money with the help of Bitcoin. Companies such as Microsoft, Destinia, WordPress, among many others, already allow purchases with bitcoins, or other cryptocurrencies, on their websites. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Quality management and labor productivity of formal companies in Perú: A non-experimental design and causal machine learning techniques(Universidad de Chile, 2024-06-01)This paper evaluates the impacts of quality management tools on the labor productivity of companies in Peru for the period 2014-2019 based on causal Machine Learning (ML) techniques (MLC), which reduce or eliminate three potential problems: the endogeneity of the variables of interest, the existence of confusing variables (confounding) and overfitting due to the of many control variables. Using the National Survey of Companies (INEI-ENE 2023), the evaluation indicates that quality control tools affect the productivity of formal companies, particularly large and medium-sized companies.