Context-Aware Parameter Estimation for Forecast Models in the Energy Domain

Continuous balancing of energy demand and supply is a fundamental prerequisite for the stability and efficiency of energy grids. This balancing task requires accurate forecasts of future electricity consumption and production at any point in time. For this purpose, database systems need to be able to rapidly process forecasting queries and to provide accurate results in short time frames. However, time series from the electricity domain pose the challenge that measurements are constantly appended to the time series. Using a naive maintenance approach for such evolving time series would mean a re-estimation of the employed mathematical forecast model from scratch for each new measurement, which is very time consuming. We speed-up the forecast model maintenance by exploiting the particularities of electricity time series to reuse previously employed forecast models and their parameter combinations. These parameter combinations and information about the context in which they were valid are stored in a repository. We compare the current context with contexts from the repository to retrieve parameter combinations that were valid in similar contexts as starting points for further optimization. An evaluation shows that our approach improves the maintenance process especially for complex models by providing more accurate forecasts in less time than comparable estimation methods

Simulating Dynamics of Circulation in the Awake State and Different Stages of Sleep Using Non-autonomous Mathematical Model With Time Delay

We propose a mathematical model of the human cardiovascular system. The model allows one to simulate the main heart rate, its variability under the influence of the autonomic nervous system, breathing process, and oscillations of blood pressure. For the first time, the model takes into account the activity of the cerebral cortex structures that modulate the autonomic control loops of blood circulation in the awake state and in various stages of sleep. The adequacy of the model is demonstrated by comparing its time series with experimental records of healthy subjects in the SIESTA database. The proposed model can become a useful tool for studying the characteristics of the cardiovascular system dynamics during sleep

The diffusion dynamics of choice: From durable goods markets to fashion first names

Goods, styles, ideologies are adopted by society through various mechanisms. In particular, adoption driven by innovation is extensively studied by marketing economics. Mathematical models are currently used to forecast the sales of innovative goods. Inspired by the theory of diffusion processes developed for marketing economics, we propose, for the first time, a predictive framework for the mechanism of fashion, which we apply to first names. Analyses of French, Dutch and US national databases validate our modelling approach for thousands of first names, covering, on average, more than 50% of the yearly incidence in each database. In these cases, it is thus possible to forecast how popular the first names will become and when they will run out of fashion. Furthermore, we uncover a clear distinction between popularity and fashion: less popular names, typically not included in studies of fashion, may be driven by fashion, as well.Comment: 11 pages, 1 table, 2 figures, 4 pages of supporting informatio

Evolutionary Events in a Mathematical Sciences Research Collaboration Network

This study examines long-term trends and shifting behavior in the collaboration network of mathematics literature, using a subset of data from Mathematical Reviews spanning 1985-2009. Rather than modeling the network cumulatively, this study traces the evolution of the "here and now" using fixed-duration sliding windows. The analysis uses a suite of common network diagnostics, including the distributions of degrees, distances, and clustering, to track network structure. Several random models that call these diagnostics as parameters help tease them apart as factors from the values of others. Some behaviors are consistent over the entire interval, but most diagnostics indicate that the network's structural evolution is dominated by occasional dramatic shifts in otherwise steady trends. These behaviors are not distributed evenly across the network; stark differences in evolution can be observed between two major subnetworks, loosely thought of as "pure" and "applied", which approximately partition the aggregate. The paper characterizes two major events along the mathematics network trajectory and discusses possible explanatory factors.Comment: 30 pages, 14 figures, 1 table; supporting information: 5 pages, 5 figures; published in Scientometric

Towards machine learning applied to time series based network traffic forecasting

This TFG will explore some specific use cases of the application of Machine Learning techniques to Software-Define Networks, in particular to overlay protocols such as LISP, VXLAN, etc.The aim of this project is to implement a network traffic forecasting model using time series and improve its performance with machine learning techniques, offering a better prediction based in outlier correction. This is a project developed in the Computer Architecture Department (DAC) at the Universitat Politècnica de Catalunya (UPC). Time Series modeling methodology is able to shape a trend and take care of any existing outlier, however it does not cover outlier impact on forecasting. In order to achieve more precision and better confidence intervals, the model combines outlier detection methodology and Artificial Neural Networks to quantify and predict outliers. A study is realized over external data to find out if there is an improvement and its effect on the predictions. Machine learning techniques as Artificial Neural Networks has proven to be an improvement of the current methodology to realize forecasting using Time Series modeling. Future work will be oriented to create an improved standard of this system focused on generalize the model.El objetivo de este proyecto es implementar un modelo de previsión de tráfico de red utilizando series temporales y mejorar su rendimiento con técnicas de aprendizaje automático, generando una mejor predicción basada en la corrección de valores atípicos. Se trata de un proyecto desarrollado en el Departamento de Arquitectura de Computadores (DAC) de la Universidad Politécnica de Cataluña (UPC). La metodología de modelado de series temporales es capaz de predecir una tendencia y hacerse cargo de cualquier valor atípico ya existente, sin embargo, no cubre el impacto de estos sobre la predicción. Con el fin de lograr una mayor precisión y mejores intervalos de confianza, el modelo combina la metodología de detección de valores atípicos y redes neuronales artificiales para cuantificar y predecir los atípicos. Un estudio se realiza sobre datos externos para averiguar si hay una mejora y su efecto sobre las predicciones. Las técnicas de aprendizaje automático, como redes neuronales artificiales, han demostrado ser una mejora de la metodología actual para realizar la predicción utilizando modelos de series de tiempo. El trabajo futuro se orientará para crear un mejor nivel de este sistema se centró en generalizar el modelo.L'objectiu d'aquest projecte és implementar un model de previsió de tràfic de xarxa utilitzant sèries temporals i millorar el seu rendiment amb tècniques d'aprenentatge automàtic, generant una millor predicció basada en la correcció de valors atípics. Es tracta d'un projecte desenvolupat al Departament d'Arquitectura de Computadors (DAC) de la Universitat Politècnica de Catalunya (UPC). La metodologia de modelatge de sèries temporals és capaç de predir una tendència i fer-se càrrec de qualsevol valor atípic ja existent, però, no cobreix l'impacte d'aquests sobre la predicció. Per tal d'aconseguir una major precisió i millors intervals de confiança, el model combina la metodologia de detecció de valors atípics i xarxes neuronals artificials per quantificar i predir els atípics. Un estudi es realitza sobre dades externes per esbrinar si hi ha una millora i el seu efecte sobre les prediccions. Les tècniques d'aprenentatge automàtic, com xarxes neuronals artificials, han demostrat ser una millora de la metodologia actual per a fer predicció utilitzant models de sèries de temps. El treball futur s'orientarà per crear un millor nivell d'aquest sistema es va centrar en generalitzar el model