Ferramenta para análise de séries temporais
Resumo
A análise de séries temporais desempenha um papel fundamental em vários setores da sociedade, suas aplicações vão desde previsões na bolsa de valores à detecção de fraudes de cartão. Esse tema faz parte do currículo de vários cursos de Educação Superior, como os cursos de Ciência da Computação, de Agronomia, de Estatística e de outros cursos correlatos. Há uma problemática em relação às ferramentas utilizadas para a análise de séries temporais na maioria desses cursos, como o uso das ferramentas R, Python ou Excel. Ao serem usadas, essas geram uma curva de aprendizado relativamente alta. Dentro desse contexto, esse trabalho busca apresentar e analisar o Metanalysis. Uma ferramenta simples que facilita a análise de séries temporais, provendo uma interface amigável para os usuários.Referências
Barnett, A., Dobson, A., (monitoring trends, W. M., and determinants in cardiovascular disease) Project (2004). Estimating trends and seasonality in coronary heart disease. Statistics in medicine, 23(22):3505–3523.
Benesty, J., Chen, J., Huang, Y., and Cohen, I. (2009). Pearson correlation coefficient. In Noise reduction in speech processing, pages 1–4. Springer.
Cryer, J. D. and Kellet, N. (1991). Time series analysis. Springer.
Ehlers, R. S. (2007). Análise de séries temporais. Universidade Federal do Paraná.
Frigge, M., Hoaglin, D. C., and Iglewicz, B. (1989). Some implementations of the boxplot. The American Statistician, 43(1):50–54.
Garrard, C., Gerrity, T., Schreiner, J., and Yeates, D. (1981). The characterization of radioaerosol deposition in the healthy lung by histogram distribution analysis. Chest, 80(6 Suppl):840–842.
Hutcheson, G. D. (2011). Ordinary least-squares regression. L. Moutinho and GD Hutcheson, The SAGE dictionary of quantitative management research, pages 224–228.
Koehler, A. B., Snyder, R. D., and Ord, J. K. (2001). Forecasting models and prediction intervals for the multiplicative holt–winters method. International Journal of Forecasting, 17(2):269–286.
Koskela, T., Lehtokangas, M., Saarinen, J., and Kaski, K. (1996). Time series prediction with multilayer perceptron, fir and elman neural networks. In Proceedings of the World Congress on Neural Networks, pages 491–496. Citeseer.
MARolA, K., KBNT, J., and Bibly, J. (1979). Multivariate analysis. AcadeInic Press, Londres.
Paunzen, E. and Vanmunster, T. (2016). Peranso–light curve and period analysis software. Astronomische Nachrichten, 337(3):239–245.
Seber, G. A. and Lee, A. J. (2012). Linear regression analysis, volume 329. John Wiley & Sons.
Shibata, R. (1976). Selection of the order of an autoregressive model by akaike’s information criterion. Biometrika, 63(1):117–126.
Sivo, S. A. (2001). Multiple indicator stationary time series models. Structural Equation Modeling, 8(4):599–612.
Van Camp, M. and Vauterin, P. (2005). Tsoft: graphical and interactive software for the analysis of time series and earth tides. Computers & Geosciences, 31(5):631–640.
Van Der Aalst, W. (2016). Data science in action. In Process mining, pages 3–23. Springer.
Welch, H. L. and Seem, J. E. (2005). System and method for filling gaps of missing data using source specified data. US Patent 6,862,540.
Benesty, J., Chen, J., Huang, Y., and Cohen, I. (2009). Pearson correlation coefficient. In Noise reduction in speech processing, pages 1–4. Springer.
Cryer, J. D. and Kellet, N. (1991). Time series analysis. Springer.
Ehlers, R. S. (2007). Análise de séries temporais. Universidade Federal do Paraná.
Frigge, M., Hoaglin, D. C., and Iglewicz, B. (1989). Some implementations of the boxplot. The American Statistician, 43(1):50–54.
Garrard, C., Gerrity, T., Schreiner, J., and Yeates, D. (1981). The characterization of radioaerosol deposition in the healthy lung by histogram distribution analysis. Chest, 80(6 Suppl):840–842.
Hutcheson, G. D. (2011). Ordinary least-squares regression. L. Moutinho and GD Hutcheson, The SAGE dictionary of quantitative management research, pages 224–228.
Koehler, A. B., Snyder, R. D., and Ord, J. K. (2001). Forecasting models and prediction intervals for the multiplicative holt–winters method. International Journal of Forecasting, 17(2):269–286.
Koskela, T., Lehtokangas, M., Saarinen, J., and Kaski, K. (1996). Time series prediction with multilayer perceptron, fir and elman neural networks. In Proceedings of the World Congress on Neural Networks, pages 491–496. Citeseer.
MARolA, K., KBNT, J., and Bibly, J. (1979). Multivariate analysis. AcadeInic Press, Londres.
Paunzen, E. and Vanmunster, T. (2016). Peranso–light curve and period analysis software. Astronomische Nachrichten, 337(3):239–245.
Seber, G. A. and Lee, A. J. (2012). Linear regression analysis, volume 329. John Wiley & Sons.
Shibata, R. (1976). Selection of the order of an autoregressive model by akaike’s information criterion. Biometrika, 63(1):117–126.
Sivo, S. A. (2001). Multiple indicator stationary time series models. Structural Equation Modeling, 8(4):599–612.
Van Camp, M. and Vauterin, P. (2005). Tsoft: graphical and interactive software for the analysis of time series and earth tides. Computers & Geosciences, 31(5):631–640.
Van Der Aalst, W. (2016). Data science in action. In Process mining, pages 3–23. Springer.
Welch, H. L. and Seem, J. E. (2005). System and method for filling gaps of missing data using source specified data. US Patent 6,862,540.
Publicado
26/10/2020
Como Citar
DA CONCEIÇÃO, Jam Sávio; DOS SANTOS, Jadson Lúcio; CAVALCANTE, Rodolfo.
Ferramenta para análise de séries temporais. In: ESCOLA REGIONAL DE COMPUTAÇÃO BAHIA, ALAGOAS E SERGIPE (ERBASE), 20. , 2020, Arapiraca-AL.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 272-281.
