Ciencias Exactas y Ciencias de la Salud
Permanent URI for this collectionhttps://hdl.handle.net/11285/551039
Pertenecen a esta colección Tesis y Trabajos de grado de las Maestrías correspondientes a las Escuelas de Ingeniería y Ciencias así como a Medicina y Ciencias de la Salud.
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- Design and implementation of a sensing platform to assess and forecast environmental conditions(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2023-05-25) Rivera Corona, Antonio Carlos; Ponce Cruz, Pedro; puemcuervo, emipsanchez; Mata Juárez, Omar; School of Engineering and Sciences; Campus Ciudad de México; Molina Gutiérrez, ArturoLocal environments are altered due to natural phenomena caused by climate change. Some nature changes are, for instance, heat waves, floods, wildfires, and air pollution, among others. Monitoring these alterations is compulsory for users who require a better understanding of their surroundings, such as farmers, transportation companies, and climate researchers. There are already solutions implemented to address this, such as fixed weather stations, satellite images, or drone mapping; However, the limitation of these approaches is that they are unaffordable for small users due to high-cost sensors and the trained personnel needed for operation and maintenance. Also, they have limited coverage and do not share information with the public. Therefore, this paper shows the design, development, and evaluation ofan open-source automatic weather station (OSWS) based on low-cost sensors that monitor environmental variables, including temperature, relative humidity, atmospheric pressure, CO2 concentration, and particulate matter (PM1, PM2.5, and PM10). This station provides a fast solution for in-site measurements for different users; it can monitor the variables remotely and forecast values within a short-time period based on the historical data captured using the ARIMA model.
- BMV stocks return prediction using macro economics variables, technical analysis, and machine learning(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2020-04-01) Hinojosa Alejandro, Ramón; TREJO RODRIGUEZ, LUIS ANGEL; 59028; Trejo Rodríguez, Luis Ángel; puemcuervo; Hervet Escobar, Laura; School of Engineering and Sciences; Campus Monterrey; Hernández Gress, NeilHistorical data, macroeconomic variables, technical analysis, and machine learning are some of the tools used to predict the price of shares of companies listed on the Mexican stock ex-change.The present thesis’s purpose is to reach a robust investment strategy, capable of coping with unforeseen events, and maximizing returns by selecting stocks quoted in the Mexican Stock Market. Our strategy predicts stock returns considering the influence of macroeconomic variables filtered by a causal analysis to determine the most significant ones, and a layered architecture, where machine learning methodologies are endowed with technical analysis applied to the stock historical data.The results from this thesis work show profitable strategies that outperform the free-risk rate of return and the Mexican Index performance. Results demonstrate even good performances when unforeseen events are present as the Covid-19 pandemic in 2020-2021.