El mundo publicitario se encuentra saturado por toda la publicidad con la que viven los consumidores diariamente. Debido a esto, en el transcurso de los años se cuentan con nuevas estrategias comunicacionales para poder llegar a los consumidores finales y atraerlos a la compra. Una de las estrategias es el Branded Content, la cual es una estrategia comunicacional que busca captar la atención de los consumidores de una manera no intrusiva; y, lo que se quiere lograr mediante el siguiente trabajo es conocer, a partir de la utilización de la estrategia del Branded Content, cómo se da la recordación de la imagen de marca por parte de los usuarios de Movistar dentro de la creación de su serie web Un día eres Jóven. Las series web forman parte del día a día de muchos usuarios, ya que nos encontramos en una era digital en donde el internet y los formatos streaming se encuentran en su máximo apogeo. Asimismo, la imagen de marca hace que los usuarios tengan un recuerdo de la marca acorde a lo que es percibido mediante su estrategia. A su vez, las organizaciones cada vez se encuentran en búsqueda de nuevas estrategias para poder obtener resultados positivos para su marca. El Branded Content junto a la imagen de marca puede que logren resultados efectivos acorde a los objetivos de la organización que favorezcan la recordación del usuario. Por consiguiente, se empleará un método de investigación cualitativo en donde se quiere obtener el resultado y/o respuestas por parte del público objetivo seleccionado sobre el uso del Branded Content dentro de las series web para generar una imagen de marca de Movistar. / The advertising world is saturated by all the publicity which consumers live daily. Due to this, over the years there are new communication strategies to reach the final consumers and attract them to the purchase. One of the strategies is branded content, which is a communication strategy that seeks to capture the attention of consumers in a non-intrusive way; And, what we want to achieve through the following work is to know, from the use of the branded content strategy, how the brand image is remembered by Movistar users within the creation of their series web Un Día eres Jóven. The web series are part of the day to day of many users, since we are in a digital era where the internet and streaming formats are at their peak. Also, the brand image makes users have a memory of the brand according to what is perceived through its strategy. In turn, organizations are increasingly looking for new strategies to obtain positive results for their brand. The Branded Content next to the brand image may achieve effective results according to the objectives of the organization that favor user remembrance. Therefore, a qualitative research method will be used where you want to obtain the result or responses from the selected target audience on the use of Branded Content within the web series to generate a Movistar brand image. / Trabajo de investigación

Les séries temporelles sont l'un des types de données les plus courants dans divers domaines scientifiques, industriels et financiers. Selon le contexte, l'analyse des séries temporelles est effectuée à diverses fins: prévision, estimation, classification et détection des tendances et des événements. Grâce aux capacités exceptionnelles de la perception visuelle humaine, la visualisation reste l'un des outils les plus puissants pour l'analyse de données, en particulier pour les données temporelles. Avec la croissance de volume et de la complexité des jeux de données, de nouvelles techniques de visualisation sont clairement nécessaires pour améliorer l'analyse des données. Elles visent à faciliter l'analyse visuelle dans le cas où des situations ou des tâches sont bien spécifiées, ou à favoriser l'analyse exploratoire non guidée.La visualisation est basée sur le "mapping visuel", un processus qui consiste à associer les valeurs de données aux canaux visuels comme la position, la taille et la couleur des éléments graphiques. A cet égard, la forme la plus connue de visualisation des séries temporelles, c'est-à-dire les graphiques linéaires ("line charts" en anglais), consiste en une mise en correspondance des valeurs de données avec la position verticale de la ligne. Cependant, un seul mapping visuel ne convient pas à toutes les situations et objectifs analytiques.Notre but est d'introduire des alternatives au mapping visuel conventionnel et de trouver des situations dans lesquelles, la nouvelle approche compense la simplicité et la familiarité des techniques existantes. Nous présentons une revue de l'état de l'art sur la visualisation des séries chronologiques, puis nous nous concentrons sur les approches existantes du mapping visuel.Ensuite, nous présentons nos contributions. Notre première contribution est une étude systématique d'un «mapping visuelle composite» qui consiste à utiliser des combinaisons de canaux visuels pour communiquer différentes facettes d'une série temporelle. Au moyen de plusieurs expériences avec des utilisateurs, nous comparons les nouveaux mappings visuels à une technique de référence existante et nous mesurons la vitesse et la précision des utilisateurs dans différentes tâches analytiques. Nos résultats montrent que les nouvelles conceptions visuelles conduisent à des performances analytiques proches de celles des techniques existantes sans être inutilement complexes ou nécessiter un entraînement. De plus, certains mappings proposés surpassent les techniques existantes dans les situations de contraintes spatiales. L'efficacité spatiale est d'une grande importance pour la visualisation simultanée de grands volumes de données ou de visualisation sur de petits écrans. Les deux scénarios font partie des défis actuels de la visualisation de l'information. / Time series are one of the most common types of recorded data in various scientific, industrial, and financial domains. Depending on the context, time series analysis are used for a variety of purposes: forecasting, estimation, classification, and trend and event detection. Thanks to the outstanding capabilities of human visual perception, visualization remains one of the most powerful tools for data analysis, particularly for time series. With the increase in data sets' volume and complexity, new visualization techniques are clearly needed to improve data analysis. They aim to facilitate visual analysis in specified situations, tasks, or for unguided exploratory analysis.Visualization is based upon visual mapping, which consists in association of data values to visual channels, e.g. position, size, and color of the graphical elements. In this regard, the most familiar form of time series visualization, i.e. line charts, consists in a mapping of data values to the vertical position of the line. However, a single visual mapping is not suitable for all situations and analytical objectives.Our goal is to introduce alternatives to the conventional visual mapping and find situations in which, the new approach compensate for the simplicity and familiarity of the existing techniques. We present a review of the existing literature on time series visualization and then, we focus on the existing approaches to visual mapping.Next, we present our contributions. Our first contribution is a systematic study of a "composite" visual mapping which consists in using combinations of visual channels to communicate different facets of a time series. By means of several user studies, we compare our new visual mappings with an existing reference technique and we measure users' speed and accuracy in different analytical tasks. Our results show that the new visual designs lead to analytical performances close to those of the existing techniques without being unnecessarily complex or requiring training. Also, some of the proposed mappings outperform the existing techniques in space constraint situations. Space efficiency is of great importance to simultaneous visualization of large volumes of data or visualization on small screens. Both scenarios are among the current challenges in information visualization.

El presente trabajo analiza el tema sobre la narrativa transmedia y el prosumidor en una serie mundialmente conocida como lo es: Game of Thrones. Es esta investigación trataremos los conceptos principales de los términos: “narrativa transmedia” y “prosumidor”. Se explicarán en dichos conceptos para informar al lector de estos nuevos significados que se ve en el nuevo medio audiovisual. En adición a ello, se hablará sobre los “nuevos medios”, mostrándoles las nuevas plataformas que se han ido creando a lo largo del tiempo y el motivo por el que estas mismas son tan atractivas para la audiencia. Finalmente, se describirá a los “nuevos consumidores”, donde se muestran a las diferentes audiencias y la creación de estas mismas. Todas estas definiciones se relacionan de manera directa con el tema de investigación, que involucra identificar el rol del prosumidor por medio de la experiencia transmedia ha logrado que la serie sea un éxito mundial. / The present study analyzes transmedia storytelling and prosumers in Game of Thrones, a world-famous series. This research will cover these two main concepts to inform readers about the new notions existing in modern audiovisual media. In addition to this, the concept of “new media” will be explained, showing new platforms that have been created over time and the reason why they are so attractive to audiences. Finally, the study will describe “new consumers” by presenting different types of audiences and their emergence. All these concepts are directly related to the research topic, which implies identifying the prosumer’s role through the transmedia experience that led the series to worldwide success. / Trabajo de investigación

Recurrent Neural Networks (RNNs) are variants of Neural Networks that are able to learn temporal relationships between sequences presented to the neural network. RNNs are often employed to learn underlying relationships in time series and sequential data. This dissertation examines the extent of RNN’s memory retention and how it is influenced by different activation functions, network structures and recurrent network types. To investigate memory retention, three approaches (and variants thereof) are used. First the number of patterns each network is able to retain is measured. Thereafter the length of retention is investigated. Lastly the previous experiments are combined to measure the retention of patterns over time. During each investigation, the effect of using different activation functions and network structures are considered to determine the configurations’ effect on memory retention. The dissertation concludes that memory retention of a network is not necessarily improved when adding more parameters to a network. Activation functions have a large effect on the performance of RNNs when retaining patterns, especially temporal patterns. Deeper network structures have the trade-off of less memory retention per parameter in favour of the ability to model more complex relationships. / Dissertation (MSc (Computer Science))--University of Pretoria, 2020. / Computer Science / MSc (Computer Science) / Unrestricted

Desarrolla los modelos de cambio de régimen de transición determinística denominado modelos SETAR, cuya principal característica es su aplicación en series que presentan ciclos límites, frecuencias dependientes de amplitud y fenómenos de salto, que no pueden ser estimadas mediante los modelos lineales de series de tiempo. La metodología empleada se basa en la propuesta por (Tong, 1978) y (Tsay, 1989) que incluye la identificación y estimación de los parámetros estructurales. Por otra parte en la hipótesis de investigación se plantea una comparación entre los modelos SETAR y los modelos GARCH, a fin de determinar el modelo más apropiado para modelar las series económicas. / Tesis

No description available.

Las expresiones idiomáticas (EI) son frases establecidas con sentido figurativo que conllevan una alta carga cultural, y, su comprensión y conocimiento permiten alcanzar un dominio más profundo del idioma. En este sentido, se espera que un traductor - intérprete alcance dicho dominio para así, enfrentar un texto o situación comunicativa, sin incurrir en errores de sentido. Frente a esto, se tomó como caso de análisis el curso de inglés TI5 para el diseño de un proyecto de investigación-acción que buscó desarrollar el manejo de EI a través del uso de series televisivas americanas. Dicha investigación permitió estudiar la realidad educativa y caracterizar las incidencias que se daban en el aula durante el proceso de enseñanza-aprendizaje de EI y, sobre todo, comprobar la efectividad de los recursos idiomáticos que sitúan al estudiante en un contexto real de la lengua. Este estudio busca tener un impacto positivo en el ámbito de enseñanza-aprendizaje dentro de la formación de traductores-intérpretes, de modo tal que, se considere el uso de material auténtico, como las series televisivas americanas, para lograr que el futuro profesional desarrolle el dominio de sus lenguas de trabajo a un nivel nativo, en la medida que comprenda los matices que suponen una lengua y su cultura. Sección obligatoria en la que el autor o los autores exponen en breves líneas lo esencial del trabajo. / Idiomatic expressions or idioms are fixed expressions with a figurative interpretation that carry a high cultural meaning; these phrases allow a deeper understanding of the language. In this light, a translator-interpreter is expected to reach that cultural level of understanding in order to face a text or interpretation, without making errors of meaning. To address this, the course of English TI5 was chosen for a case analysis, in order to apply an action research study whose purpose was to implement the use of idioms through American TV series. This research allowed to analyze the educational reality and characterize the occurrences that take place during the learning process of idioms in the classroom and, most of all, to verify the effectiveness of these cultural resources when placing a student in a real context of the language. This research seeks to have a positive impact in the teaching-learning process within the formation of translators-interpreters, so the use of authentic material, such as American TV series, will be considered for the mastery of their working languages, to reach a native level where not only the language is fully understood, but its culture as well. / Trabajo de suficiencia profesional

Time series prediction plays a pivotal role in various areas, including for example finance, weather forecasting, and traffic analysis. In this study, time series of historical sales data from a packaging manufacturer is used to investigate the effects that clustering such data has on forecasting performance. An experiment is carried out in which the time series data is first clustered using two separate approaches: k-means and Self-Organizing Map (SOM). The clustering is feature-based, meaning that characteristics extracted from the time series are used to compute similarity, rather than the raw time series. Then, A set of Long Short-Term Memory models (LSTMs) are trained; one that is trained on the entire dataset (global model), separate models trained on each of the clusters (cluster-based models), and finally a number of models trained on individual time series that are proportionally sampled from the clusters (single models). By evaluating the LSTMs based on Mean Absolute Error (MAE) and Mean Squared Error (MSE), we assess their consistency and predictive potential. The results reveal a trade-off between the consistency and predictive performance of the models. The global LSTM model consistently exhibits more stable performance across all predictions, showcasing its ability to capture the overall patterns in the data. However, the cluster-based LSTM models demonstrate potential for improved predictive performance within specific clusters, albeit with higher variability. This suggests that certain clusters possess distinct characteristics that allow for better predictions within those subsets of the data. Finally, the single LSTM models trained on individual time series, showcase even wider spreads of scores. The analysis suggests that the availability of training data plays a crucial role in the robustness (i.e., the ability to consistently produce similar results) of the forecasting models, with the global model benefiting from a larger training set. The higher variability in performance seen for the models trained with smaller training sets indicates that certain time series may be easier or harder to predict. It seems that the noise that comes with a larger training set can be either beneficial or detrimental to the predictive performance of the forecasting model on any individual time series, depending on the characteristics of that particular sample. Further analysis is required to investigate the factors contributing to the varying performance within each cluster. Exploring feature scores associated with poorly performing clusters and identifying the key features that contribute to better performance in certain clusters could provide valuable insights. Understanding these factors might aid in developing tailored strategies for cluster-specific prediction tasks.

[pt] Muito freqüentemente, as técnicas utilizadas na identificação de processos estocásticos conduzem a mais de um modelo passível de ser utilizado na caracterização do processo. O problema de escolher entre estes modelos é formulado como um problema de teste de hipóteses, e o teste de razão de verossimilhança é a ele aplicado. Considera-se então a situação particular onde se quer descrever processos de parâmetro discreto (séries temporais) através de modelos ARIMA (autoregressive Integrated Moving Average). O teste de razão de verossimilhança associado ao problema é então deduzido e implementado através do algoritmo de Kalman-Bucy. Comparações com um outro teste usualmente empregado na escolha de modelos para séries temporais mostram a superioridade do teste de razão de verossimilhança. / [en] Very often random process identification techniques lead to several prospective models to characterize the process. The problem of choosing among these models is cast as a hypothesis testing problem, to which a likelihood ratio test is applied. For the special situation in which a choice between two autoregressive integrated moving average models is to made, likelihood ratio is derived and afterwards implemented through the Kalman-Bucy algorithm. Comparisons with another procedure usually connected to time series model choices show likelihood ratio tests are definetely superior.

Pratima Sadangi in a Ph.D. thesis submitted to Utkal University proved results on degree of approximation of functions by operators associated with their Fourier series. In this dissertation, we consider degree of approximation of functions in Hα,ρ by different operators. In Chapter 1 we mention basic definitions needed for our work. In Chapter 2 we discuss different methods of summation. In Chapter 3 we define the Hα,ρ metric and present the degree of approximation problem relating to Fourier series and conjugate series of functions in the Hα,ρ metric using Karamata (Κλ) means. In Chapter 4 we present the degree of approximation of an integral associated with the conjugate series by the Euler, Borel and (e,c) means of a series analogous to the Hardy-Littlewood series in the Hα,ρ metric. In Chapter 5 we propose problems to be solved in the future.