Efficient Forecasting for Hierarchical Time Series

Forecasting is used as the basis for business planning in many application areas such as energy, sales and traffic management. Time series data used in these areas is often hierarchically organized and thus, aggregated along the hierarchy levels based on their dimensional features. Calculating forecasts in these environments is very time consuming, due to ensuring forecasting consistency between hierarchy levels. To increase the forecasting efficiency for hierarchically organized time series, we introduce a novel forecasting approach that takes advantage of the hierarchical organization. There, we reuse the forecast models maintained on the lowest level of the hierarchy to almost instantly create already estimated forecast models on higher hierarchical levels. In addition, we define a hierarchical communication framework, increasing the communication flexibility and efficiency. Our experiments show significant runtime improvements for creating a forecast model at higher hierarchical levels, while still providing a very high accuracy

Fluid Petri Nets for the Performance Evaluation of MapReduce Applications

Big Data applications allow to successfully analyze large amounts of data not necessarily structured, though at the same time they present new challenges. For example, predicting the performance of frameworks such as Hadoop can be a costly task, hence the necessity to provide models that can be a valuable support for designers and developers. This paper provides a new contribution in studying a novel modeling approach based on fluid Petri nets to predict MapReduce jobs execution time. The experiments we performed at CINECA, the Italian supercomputing center, have shown that the achieved accuracy is within 16% of the actual measurements on average

Nonlinear Hyperspectral Unmixing With Robust Nonnegative Matrix Factorization

International audienceWe introduce a robust mixing model to describe hyperspectral data resulting from the mixture of several pure spectral signatures. The new model extends the commonly used linear mixing model by introducing an additional term accounting for possible nonlinear effects, that are treated as sparsely distributed additive outliers.With the standard nonnegativity and sum-to-one constraints inherent to spectral unmixing, our model leads to a new form of robust nonnegative matrix factorization with a group-sparse outlier term. The factorization is posed as an optimization problem which is addressed with a block-coordinate descent algorithm involving majorization-minimization updates. Simulation results obtained on synthetic and real data show that the proposed strategy competes with state-of-the-art linear and nonlinear unmixing methods

A Time-Series Compression Technique and its Application to the Smart Grid

Time-series data is increasingly collected in many domains. One example is the smart electricity infrastructure, which generates huge volumes of such data from sources such as smart electricity meters. Although today this data is used for visualization and billing in mostly 15-min resolution, its original temporal resolution frequently is more fine-grained, e.g., seconds. This is useful for various analytical applications such as short-term forecasting, disaggregation and visualization. However, transmitting and storing huge amounts of such fine-grained data is prohibitively expensive in terms of storage space in many cases. In this article, we present a compression technique based on piecewise regression and two methods which describe the performance of the compression. Although our technique is a general approach for time-series compression, smart grids serve as our running example and as our evaluation scenario. Depending on the data and the use-case scenario, the technique compresses data by ratios of up to factor 5,000 while maintaining its usefulness for analytics. The proposed technique has outperformed related work and has been applied to three real-world energy datasets in different scenarios. Finally, we show that the proposed compression technique can be implemented in a state-of-the-art database management system

Tree Growth Dynamics, Fire History, and Fire-Climate Relationships in Pine Rocklands of the Florida Keys, U.S.A.

Pine rocklands are globally endangered, fire-maintained communities currently restricted to small habitat areas in southern Florida, Cuba, and the Bahamas. The purpose of this dissertation research was to identify the long-term ecological disturbance regimes and climatic trends responsible for the persistence of pine rocklands, and examine how human-induced changes during the 20th century contributed to decline of these communities. This research applied techniques of dendrochronology in extreme southern Florida, in a subtropical region where tree‐ring science has never been applied, to increase the understanding of how anthropogenic and natural disturbance events have decreased the spatial distribution of South Florida slash pine (Pinus elliottii Engelm. var. densa Little and Dorman; hereafter slash pine), the foundation species of pine rocklands. To investigate the complex dynamics of declining pine rockland communities, I analyzed (1) the dendrochronological potential and climate response of slash pine, (2) the intra-annual ring formation characteristics and relationships to monthly climatic conditions, (3) the influence of historical fire regimes and varied fire management practices since the 1950s on the structure of slash pine savannas on adjacent islands in the Lower Florida Keys, and (4) the control of global-scale oceanic/atmospheric oscillations on historical wildfire occurrence. The analyses presented here demonstrate that slash pine forms anatomically distinct, annual growth rings with the consistent year-to-year variability necessary for rigorous dendrochronological studies. Annual radial growth of slash pine is primarily influenced by water availability during the growing season; however intra-annual cellular growth is driven by daily insolation. In the Lower Florida Keys, the growing season of slash pine occurs between February and November, with trees experiencing dormancy between November and January. Reconstructions of fire history and savanna structure revealed that, over the past 150 years, frequent fires occurring every ca. 6 years promoted pine recruitment and ensured the persistence of pine rockland habitat. However, the recent lack of fire in some areas could result in the loss of pine rockland habitat, as pine savannas are currently succeeding to tropical hardwood hammock. Over the past several centuries, interacting effects of two Pacific climatic forcing mechanisms (El Niño-Southern Oscillation, Pacific Decadal Oscillation) drove wildfire occurrence in the Lower Florida Keys

Reengineering and development of IoT Systems for Home Automation

BEng Thesis, Instituto Superior de Engenharia do Porto.With the increasing adoption of technology in today’s houses, electricity is at an all-time high demand. In fact, given the plethora of vital electricity-powered appliances used every day, such as refrigerators, washing machines, and so forth, it has been proven difficult to even handle all devices’ electric consumption. To reduce consumption costs and turn it into a more manageable process, the concept of flex-offers was created. A flex-offer is built around scheduling energy usage in conjunction with the prices of electricity, as provided by an energy market. More specifically, a flex-offer is an energy consumption offer containing the user’s energy consumption flexibility, which is sent to an entity called the Aggregator, who aggregates together flex-offers from multiple parties, bargains with the energy market, and responds to each flex-offer with a schedule that meets the lowest prices for consumption, while still satisfying the users’ needs. By using flex-offers on a house’s equipment, the idea of FlexHousing was born. The aspired goal of the CISTER Research Center’s FlexHousing project is to deliver a platform where users can register their smart appliances, regardless of its brand and distributor, set up preferences for the devices’ usage, and let the system manage the energy consumption and device activation schedules based on the energy market prices. A previous project had already built a prototype of the FlexHousing system. Nevertheless, the original platform had many limitations and lacked maturity from a software engineering point of view, and the goal of this internship is to apply a reengineering process on the FlexHousing project, while also adding new features to it. Thus, the project’s domain model, its database, and class structures were altered to satisfy the new requirements. Furthermore, its web platform was rebuilt from the ground up. Also, a new interface was developed to facilitate support for devices of different brands. As a proof of concept for the benefits provided by this new interface, a connection with a new device (Sonoff Pow) was also established. Moreover, a new functionality was developed to identify a device’s type of appliance based on its energy consumption, in other words, to specify if a device is, for instance, a refrigerator or not. Finally, another new feature was added in which, based on a device’s type and its energy consumption pattern, the flex-offer creation is automated, minimizing user input. As planned, the FlexHousing platform now supports multiple types of devices, and has a software interface to support more types in the future with minimal effort. The flex-offer creation process has been simplified and is now partially automated. Finally, the web platform’s UI has been updated, becoming more intuitive and appealing to the user.info:eu-repo/semantics/publishedVersio

Index for Senate and House Journals, 1984

Index for Senate and House Journals