Digestão anaeróbia de resíduos agroalimentares: reutilização das frações líquidas e sólidas (state-of-art)

Dissertação para obtenção do Grau de Mestre em Energia e Bioenergia

Native american women in sherman Alexie's short stories: stereotypical representations

Dissertação apresentada para cumprimento dos requisitos necessários à obtenção do grau de Mestre em Línguas, Literaturas e Culturas

Air lime-earth blended mortars - Assessment on fresh state and workability

International Conference on Vernacular Heritage, Sustainability and Earthen Architecture, VerSus 2014, 2nd MEDITERRA, 2nd ResTAPIA, 11-13 September, Valencia, Spain

State detection in a financial portfolio: a self-organizing maps approach for financial time series

This study analyses financial data using the result characterization of a self-organized neural network model. The goal was prototyping a tool that may help an economist or a market analyst to analyse stock market series. To reach this goal, the tool shows economic dependencies and statistics measures over stock market series. The neural network SOM (self-organizing maps) model was used to ex-tract behavioural patterns of the data analysed. Based on this model, it was de-veloped an application to analyse financial data. This application uses a portfo-lio of correlated markets or inverse-correlated markets as input. After the anal-ysis with SOM, the result is represented by micro clusters that are organized by its behaviour tendency. During the study appeared the need of a better analysis for SOM algo-rithm results. This problem was solved with a cluster solution technique, which groups the micro clusters from SOM U-Matrix analyses. The study showed that the correlation and inverse-correlation markets projects multiple clusters of data. These clusters represent multiple trend states that may be useful for technical professionals.