El presente trabajo de investigación tiene como finalidad analizar el incremento de suscriptores de Netflix con respecto a la competencia desde el 2010 hasta lo que va del año 2020. Hemos determinado que el enfoque será predictivo para que la organización a cargo pueda hacer uso del modelo supervisado de la manera que más le favorezca y estos puedan tomar las mejores decisiones estratégicas. Para ello, se ha generado una base de datos recopilada de diversas fuentes públicas confiables para obtener las variables: “cantidad de suscriptores”, “costo de contenido original”, “covid-19” … y posterior a ello, con toda la data adquirida se procederá a realizar cada etapa de la metodología de la ciencia de datos descrita en el curso durante el programa de ciencia de datos. Para aclarar el panorama hemos optado por el uso de la técnica de correlación de Pearson, lo cual nos permitió determinar las variables que tenían mejor correlación entre ellas, esto advierte que la variable más adecuada para determinar futuros pronósticos y analizar el incremento de suscriptores es la del costo de contenido original. Finalmente, para mostrar los resultados de la investigación se ha decidido utilizar como herramienta de visualización Power BI para exponer el presente estudio y responder a los objetivos planteados. / The purpose of this research work is to analyze the increase in Netflix subscribers with respect to the competition from 2010 to so far in 2020. We have determined that the approach will be predictive so that the organization in charge can make use of the supervised model in the way that best suits them and they can make the best strategic decisions. For this, a database compiled from various reliable public sources has been generated to obtain the variables: "number of subscribers", "cost of original content", "covid-19" ... and after that, with all the data acquired Each stage of the data science methodology described in the course will be carried out during the data science program. To clarify the panorama we have opted for the use of the Pearson correlation technique, which allowed us to determine the variables that had the best correlation between them, this warns that the most appropriate variable to determine future forecasts and analyze the increase in subscribers is the of the cost of original content. Finally, to show the results of the research, it has been decided to use Power BI as a visualization tool to present the present study and respond to the objectives set. / Trabajo de investigación

The load forecasting has become an important role in the operation of power system, and several models by using different techniques have been applied to solve these problems. In the literature, the linear regression models are considered as a traditional approach to predict power consumption, and more recently, the artificial neural network (ANN) models have received more attention for a great number of successful and practical applications. This report introduces both linear regression and ANN models to predict the power consumption for Fortum in Ekerö. The characteristics of power consumption of different kinds of consumers are analyzed, together with the effects of weather parameters to power consumption. Further, based on the gained information, the numerical models of load forecasting are built and tested by the historical data. The predictions of power consumption are focus on three cases separately: total power consumption in one year, daily peak power consumption during winter and hourly power consumption. The processes of development of the models will be described, such as the choice of the variables, the transformations of the variables, the structure of the models and the training cases of ANN model. In addition, two linear regression models will be built according to the number of input variables. They are simple linear regression with one input variable and multiple linear regression with several input variables. Comparison between the linear regression and ANN models will be carried out. In the end, it finds out that the linear regression obtains better results for all the cases in Ekerö. Especially, the simple linear regression outperforms in prediction of total power consumption in one year, and the multiple linear regression is better in prediction of daily peak load during the winter.

Submitted by Johnny Rodrigues (johnnyrodrigues@ufcg.edu.br) on 2018-04-11T14:50:08Z No. of bitstreams: 1 CAIO SANTOS BEZERRA NÓBREGA - DISSERTAÇÃO PPGCC 2014..pdf: 983246 bytes, checksum: 5eca7651706ce317dc514ec2f1aa10c3 (MD5) / Made available in DSpace on 2018-04-11T14:50:08Z (GMT). No. of bitstreams: 1 CAIO SANTOS BEZERRA NÓBREGA - DISSERTAÇÃO PPGCC 2014..pdf: 983246 bytes, checksum: 5eca7651706ce317dc514ec2f1aa10c3 (MD5) Previous issue date: 2014-07-30 / Capes / Sugerir os produtos mais apropriados aos diversos tipos de consumidores não é uma tarefa trivial, apesar de ser um fator chave para aumentar satisfação e lealdade destes. Devido a esse fato, sistemas de recomendação têm se tornado uma ferramenta importante para diversas aplicações, tais como, comércio eletrônico, sites personalizados e redes sociais. Recentemente, a fatoração de matrizes se tornou a técnica mais bem sucedida de implementação de sistemas de recomendação. Os parâmetros do modelo de fatoração de matrizes são tipicamente aprendidos por meio de métodos numéricos, tal como o gradiente descendente. O desempenho do gradiente descendente está diretamente relacionada à configuração da taxa de aprendizagem, a qual é tipicamente configurada para valores pequenos, com o objetivo de não perder um mínimo local. Consequentemente, o algoritmo pode levar várias iterações para convergir. Idealmente,é desejada uma taxa de aprendizagem que conduza a um mínimo local nas primeiras iterações, mas isto é muito difícil de ser realizado dada a alta complexidade do espaço de valores a serem pesquisados. Começando com um estudo exploratório em várias bases de dados de sistemas de recomendação, observamos que, para a maioria das bases, há um padrão linear entre a taxa de aprendizagem e o número de iterações necessárias para atingir a convergência. A partir disso, propomos utilizar modelos de regressão lineares simples para predizer, para uma base de dados desconhecida, um bom valor para a taxa de aprendizagem inicial. A ideia é estimar uma taxa de aprendizagem que conduza o gradiente descendenteaummínimolocalnasprimeirasiterações. Avaliamosnossatécnicaem8bases desistemasderecomendaçãoreaisecomparamoscomoalgoritmopadrão,oqualutilizaum valorfixoparaataxadeaprendizagem,ecomtécnicasqueadaptamataxadeaprendizagem extraídas da literatura. Nós mostramos que conseguimos reduzir o número de iterações até em 40% quando comparados à abordagem padrão. / Suggesting the most suitable products to different types of consumers is not a trivial task, despite being a key factor for increasing their satisfaction and loyalty. Due to this fact, recommender systems have be come an important tool for many applications, such as e-commerce, personalized websites and social networks. Recently, Matrix Factorization has become the most successful technique to implement recommendation systems. The parameters of this model are typically learned by means of numerical methods, like the gradient descent. The performance of the gradient descent is directly related to the configuration of the learning rate, which is typically set to small values, in order to do not miss a local minimum. As a consequence, the algorithm may take several iterations to converge. Ideally, one wants to find a learning rate that will lead to a local minimum in the early iterations, but this is very difficult to achieve given the high complexity of search space. Starting with an exploratory study on several recommendation systems datasets, we observed that there is an over all linear relationship between the learnin grate and the number of iterations needed until convergence. From this, we propose to use simple linear regression models to predict, for a unknown dataset, a good value for an initial learning rate. The idea is to estimate a learning rate that drives the gradient descent as close as possible to a local minimum in the first iteration. We evaluate our technique on 8 real-world recommender datasets and compared it with the standard Matrix Factorization learning algorithm, which uses a fixed value for the learning rate over all iterations, and techniques fromt he literature that adapt the learning rate. We show that we can reduce the number of iterations until at 40% compared to the standard approach.

[pt] O objetivo desta dissertação é desenvolver modelos matemáticos que permitam estimar o tempo médio dos banhos com a utilização de chuveiros elétricos e a curva de carga desses aparelhos, considerando as informações das Pesquisas de Posses e Hábitos de Consumo (PPH) e medições realizadas com o auxílio de medidores eletrônicos com memória de massa, em residências com chuveiros elétricos. A motivação do estudo advém de uma exigência da ANEEL que determina que as distribuidoras de energia elétrica realizem a cada 2 (dois) ciclos de revisão tarifária a PPH em suas unidades consumidoras. Os métodos empregados foram: estatística descritiva (para a obtenção do tempo médio de banho); aplicação da regressão linear e de redes neurais (para corrigir a curva de carga horária obtida com a PPH, com base nos dados das medições). Os resultados foram promissores, pois o tempo médio de banho se encontra próximo às estimativas do PROCEL (que são de 8 (oito) a 10 (dez) minutos) e a curva de carga estimada se encontra próxima à da medição, sendo esta última o consumo real. Conclui-se que a abordagem desta dissertação resultou em melhorias na estimativa dos coeficientes de ajustes e que o método de redes neurais foi relativamente melhor que o método de regressão linear simples. / [en] The aim of this dissertation is to develop mathematical models that would allow the estimation of the average time of baths using electric showers and the load shape curves for these devices, obtained from two sources: the information of Electrical Appliances Ownership Survey and measurements of electric shower usage in households carried out with electronic meters with storage capacity. The motivation stems from a requirement of ANEEL that determines that the electric energy distributors periodically should hold a PPH in their consumer units. Concerning the average time of shower baths, the last PPH survey conducted by PROCEL in 2005 estimated this time between 8 (eight) and 10 (ten) minutes. The methods employed in this work were: descriptive statistics (for obtaining the average bath time); application of linear regression and neural networks (to estimate the correction factors to approximate the load shape curves obtained by PPH to those obtained by measurements). The obtained results are rather promising due to the following reasons: the average time of bath is next to the estimates of PROCEL and the corrected load shape curve estimated is quite close to the measured curve, the latter being the actual consumption. This approach has resulted in improvements in the estimation of the coefficients of adjustments and the method of neural networks was relatively better than the simple linear regression method.