Coleção de Artigos Acadêmicos

URI permanente para esta coleçãohttps://repositorio.insper.edu.br/handle/11224/3227

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2022 - 20232

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Palavra-chave: search.filters.subject.Deep learning

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Agora exibindo 1 - 2 de 2
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    Artigo Científico
    Deep learning models for inflation forecasting
    (2023) Theoharidis, Alexandre Fernandes; DIOGO ABRY GUILLEN; HEDIBERT FREITAS LOPES; Hosszejni, Darjus
    We propose a hybrid deep learning model that merges Variational Autoencoders and Convolutional LSTM Networks (VAE-ConvLSTM) to forecast inflation. Using a public macroeconomic database that comprises 134 monthly US time series from January 1978 to December 2019, the proposed model is compared against several popular econometric and machine learning benchmarks, including Ridge regression, LASSO regression, Random Forests, Bayesian methods, VECM, and multilayer perceptron. We find that VAE-ConvLSTM outperforms the competing models in terms of consistency and out-of-sample performance. The robustness of such conclusion is ensured via cross-validation and Monte-Carlo simulations using different training, validation, and test samples. Our results suggest that macroeconomic forecasting could take advantage of deep learning models when tackling nonlinearities and nonstationarity, potentially delivering superior performance in comparison to traditional econometric approaches based on linear, stationary models.
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    Extracting value from Brazilian Court decisions
    (2022) Fernandes, William Paulo Ducca; Frajhof, Isabella Zalcberg; GUILHERME DA FRANCA COUTO FERNANDES DE ALMEIDA; Rodrigues, Ariane Moraes Bueno; Barbosa, Simone Diniz Junqueira; Konder, Carlos Nelson; Nasser, Rafael Barbosa; Carvalho, Gustavo Robichez de; Lopes, Hélio Côrtes Vieira
    We propose a methodology to extract value from Brazilian Court decisions to support judges and lawyers in their decision-making. We instantiate our methodology in one information system we have developed. Such system (i) extracts plaintiff’s legal claims and each specific provision on legal opinions enacted by lower and Appellate Courts, and (ii) connects each legal claim with the corresponding judicial provision. The information system presents the results through visualizations. Information Extraction for legal texts has been previously approached in the literature for different languages, using different methods. Our proposal is different from previous work, since our corpora comprise Brazilian lower and Appellate Court decisions, in which we look for a set of plaintiff’s legal claims and judicial provisions commonly judged by the Court. We use the following methods to tackle the information extraction tasks: Bidirectional Long Short-Term Memory network; Conditional Random Fields; and a combination of Bidirectional Long Short-Term Memory network and Conditional Random Fields. In addition to the well-known distributed representation of words in word embeddings, we use character-level representation of words in character embeddings. We have built three corpora – Kauane Insurance Report, Kauane Insurance Lower, and Kauane Insurance Upper – to train and evaluate the system, using public data from the State Court of Rio de Janeiro. Our methods achieved good quality for Kauane Insurance Lower and Kauane Insurance Upper, and promising results for Kauane Insurance Report.