Institution
Polytechnic University of Catalonia
Education•Barcelona, Spain•
About: Polytechnic University of Catalonia is a education organization based out in Barcelona, Spain. It is known for research contribution in the topics: Finite element method & Population. The organization has 16006 authors who have published 45325 publications receiving 949306 citations. The organization is also known as: UPC - BarcelonaTECH & Technical University of Catalonia.
Topics: Finite element method, Population, Context (language use), Computer science, Nonlinear system
Papers published on a yearly basis
Papers
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TL;DR: Whether a public sentiment indicator extracted from daily Twitter messages can indeed improve the forecasting of social, economic, or commercial indicators is assessed and nonlinear models do take advantage of Twitter data when forecasting trends in volatility indices, while linear ones fail systematically when forecasting any kind of financial time series.
Abstract: The dramatic rise in the use of social network platforms such as Facebook or Twitter has resulted in the availability of vast and growing user-contributed repositories of data. Exploiting this data by extracting useful information from it has become a great challenge in data mining and knowledge discovery. A recently popular way of extracting useful information from social network platforms is to build indicators, often in the form of a time series, of general public mood by means of sentiment analysis. Such indicators have been shown to correlate with a diverse variety of phenomena. In this article we follow this line of work and set out to assess, in a rigorous manner, whether a public sentiment indicator extracted from daily Twitter messages can indeed improve the forecasting of social, economic, or commercial indicators. To this end we have collected and processed a large amount of Twitter posts from March 2011 to the present date for two very different domains: stock market and movie box office revenue. For each of these domains, we build and evaluate forecasting models for several target time series both using and ignoring the Twitter-related data. If Twitter does help, then this should be reflected in the fact that the predictions of models that use Twitter-related data are better than the models that do not use this data. By systematically varying the models that we use and their parameters, together with other tuning factors such as lag or the way in which we build our Twitter sentiment index, we obtain a large dataset that allows us to test our hypothesis under different experimental conditions. Using a novel decision-tree-based technique that we call summary tree we are able to mine this large dataset and obtain automatically those configurations that lead to an improvement in the prediction power of our forecasting models. As a general result, we have seen that nonlinear models do take advantage of Twitter data when forecasting trends in volatility indices, while linear ones fail systematically when forecasting any kind of financial time series. In the case of predicting box office revenue trend, it is support vector machines that make best use of Twitter data. In addition, we conduct statistical tests to determine the relation between our Twitter time series and the different target time series.
158 citations
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TL;DR: The class of binary operations \/o on distribution functions which are both induced pointwise, and derivable from functions on random variables (e.g. mixtures), is characterized.
158 citations
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TL;DR: In this article, a "one pot" facile method for environmentally benign production of stable Ag colloids, using short chain polyethylene glycol as solvent, reducing agent and stabilizer, was reported.
158 citations
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National Renewable Energy Laboratory1, University of Maine2, Maritime Research Institute Netherlands3, DNV GL4, Technical University of Denmark5, French Institute of Petroleum6, Polytechnic University of Milan7, Siemens PLM Software8, University of Cantabria9, University of Ulsan10, University of Tokyo11, Polytechnic University of Catalonia12
TL;DR: In this paper, the authors present the results from Phase II of the Offshore Code Comparison, Collaboration, Continued, with Correlation (OCC) project, where numerical models of the DeepCwind floating semisubmersible wind system were validated using measurement data from a 1/50th-scale validation campaign performed at the Maritime Research Institute Netherlands offshore wave basin.
158 citations
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TL;DR: A downscaling algorithm is presented as a new ability to obtain multiresolution soil moisture estimates from SMOS using visible-to-infrared remotely sensed observations to enhance the spatial resolution of SMOS observations over semi-arid regions such as the Iberian Peninsula.
Abstract: 13 pages, 6 figures, 1 table.-- © 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works
158 citations
Authors
Showing all 16211 results
Name | H-index | Papers | Citations |
---|---|---|---|
Frede Blaabjerg | 147 | 2161 | 112017 |
Carlos M. Duarte | 132 | 1173 | 86672 |
Ian F. Akyildiz | 117 | 612 | 99653 |
Josep M. Guerrero | 110 | 1197 | 60890 |
David S. Wishart | 108 | 523 | 76652 |
O. C. Zienkiewicz | 107 | 455 | 71204 |
Maciej Lewenstein | 104 | 931 | 47362 |
Jordi Rello | 103 | 694 | 35994 |
Anil Kumar | 99 | 2124 | 64825 |
Surendra P. Shah | 99 | 710 | 32832 |
Liang Wang | 98 | 1718 | 45600 |
Aharon Gedanken | 96 | 861 | 38974 |
María Vallet-Regí | 95 | 711 | 41641 |
Bonaventura Clotet | 94 | 784 | 39004 |
Roberto Elosua | 90 | 481 | 54019 |