{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T20:34:38Z","timestamp":1761165278054,"version":"build-2065373602"},"reference-count":16,"publisher":"Sociedade Brasileira de Computa\u00e7\u00e3o - SBC","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:p>Este trabalho apresenta um estudo explorat\u00f3rio sobre a aplica\u00e7\u00e3o do modelo ARIMA diretamente no banco de dados Vertica para previs\u00e3o da velocidade do vento. Utilizou-se um conjunto de dados do projeto EOSOLAR, com medi\u00e7\u00f5es de perfis verticais de vento na regi\u00e3o costeira do Maranh\u00e3o. A avalia\u00e7\u00e3o considerou as m\u00e9tricas RMSE (Root Mean Squared Error) e MAE (Mean Absolute Error) sobre 105 modelos treinados. O estudo investigou se a abordagem in-database do ARIMA no Vertica poderia oferecer modelagem eficiente para a previs\u00e3o da velocidade do vento. Os resultados mostraram que modelos com baixa complexidade alcan\u00e7aram bom desempenho preditivo.<\/jats:p>","DOI":"10.5753\/sbbd.2025.247487","type":"proceedings-article","created":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T19:26:36Z","timestamp":1761074796000},"page":"746-752","source":"Crossref","is-referenced-by-count":0,"title":["Aplica\u00e7\u00e3o do Modelo ARIMA no Vertica para Previs\u00e3o da Velocidade do Vento"],"prefix":"10.5753","author":[{"given":"Gabriel Ciriaco","family":"Fornitano","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fl\u00e1vio Beliz\u00e1rio","family":"Mota","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vanessa Cristina Oliveira de","family":"Souza","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Arcilan","family":"Assireu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Melise Maria","family":"Veiga de Paula","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"3742","published-online":{"date-parts":[[2025,9,29]]},"reference":[{"key":"1","doi-asserted-by":"crossref","unstructured":"Assireu, A. 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In Estruturas pr\u00e9-moldadas no mundo \u2013 Aplica\u00e7\u00f5es e comportamento estrutural, pages 91\u2013106. Universidade NOVA de Lisboa."},{"key":"5","unstructured":"Cielen, D., Meysman, A. D. B., and Ali, M. (2021). Data Science: Principles and Practice. Manning Publications."},{"key":"6","doi-asserted-by":"crossref","unstructured":"Elsaraiti, M. and Merabet, A. (2021). A comparative analysis of the arima and lstm predictive models and their effectiveness for predicting wind speed. Energies, 14(20):6782.","DOI":"10.3390\/en14206782"},{"key":"7","unstructured":"Epstein, B. and Roberts, P. (2022). Accelerate Machine Learning with a Unified Analytics Architecture. O\u2019Reilly Media, Inc., Sebastopol, CA, USA."},{"key":"8","doi-asserted-by":"crossref","unstructured":"Fard, A., Zhang, B., Katepalli, K., Stonebraker, M., and Rundensteiner, E. A. (2020). Vertica-ml: Distributed machine learning in vertica database. In Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data, pages 755\u2013768. ACM.","DOI":"10.1145\/3318464.3386137"},{"key":"9","doi-asserted-by":"crossref","unstructured":"Grigonyte\u0307, E. and Butkevi\u010diu\u0304te\u0307, E. (2016). Short-term wind speed forecasting using arima model. Energetika, 62(1\u20132):17\u201326.","DOI":"10.6001\/energetika.v62i1-2.3313"},{"key":"10","unstructured":"Hyndman, R. J. and Athanasopoulos, G. (2021). Forecasting: Principles and Practice. OTexts, Melbourne, Australia, 3 edition. Accessed on March 26, 2025."},{"key":"11","doi-asserted-by":"crossref","unstructured":"Lamb, A., Fuller, M., Varadarajan, R., Tran, N., Vandiver, B., Doshi, L., and Bear, C. (2012). The vertica analytic database: C-store 7 years later. Vertica Systems, An HP Company.","DOI":"10.14778\/2367502.2367518"},{"key":"12","doi-asserted-by":"crossref","unstructured":"Liu, X., Lin, Z., and Feng, Z. (2021). Short-term offshore wind speed forecast by seasonal arima-a comparison against gru and lstm. Energy, 227:120492.","DOI":"10.1016\/j.energy.2021.120492"},{"key":"13","unstructured":"Lustosa, H., Costa, F., Guimar\u00e3es, J., and de Oliveira, D. (2020). Savime: An array dbms for simulation analysis and ml models predictions. In International Conference on Database and Expert Systems Applications, pages 357\u2013367. Springer."},{"key":"14","doi-asserted-by":"crossref","unstructured":"Raschka, S., Patterson, J., and Nolet, C. (2020). Machine learning in python: Main developments and technology trends in data science, machine learning, and artificial intelligence. Information, 11(4):193.","DOI":"10.3390\/info11040193"},{"key":"15","doi-asserted-by":"crossref","unstructured":"Salman, A. G. and Kanigoro, B. (2021). Visibility forecasting using autoregressive integrated moving average (arima) models. 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