Forecasting commodity prices in Brazil through hybrid SSA-complex seasonality models
Rafael Baptista Palazzi; Paula Maçaira; Erick Meira; Marcelo Cabus Klotzle
Abstract
Keywords
References
Athoillah, I., Wigena, A. H., & Wijayanto, H. (2021). Hybrid modeling of singular spectrum analysis and support vector regression for rainfall prediction.
Box, G. E. P., & Jenkins, G. M. (1970).
Cleveland, R. B., Cleveland, W. S., McRae, J. E., & Terpenning, I. (1990). STL: a seasonal-trend decomposition procedure based on loess (with discussion).
Companhia Nacional de Abastecimento - CONAB. (2021).
Crone, S. F., & Kourentzes, N. (2010). Feature selection for time series prediction: a combined filter and wrapper approach for neural networks.
Degiannakis, S., Filis, G., & Hassani, H. (2018). Forecasting global stock market implied volatility indices.
Demšar, J. (2006). Statistical comparisons of classifiers over multiple data sets.
Ding, S., Zhao, H., Zhang, Y., Xu, X., & Nie, R. (2015). Extreme learning machine: algorithm, theory and applications.
Fang, Y., Guan, B., Wu, S., & Heravi, S. (2020). Optimal forecast combination based on ensemble empirical mode decomposition for agricultural commodity futures prices.
Fathi, A. Y., El-Khodary, I. A., & Saafan, M. (2022). Integrating singular spectrum analysis and nonlinear autoregressive neural network for stock price forecasting.
Fildes, R., & Allen, P. G. (2015). Forecasting. In R. W. Griffin (Ed.),
Gardner, M. W., & Dorling, S. R. (1998). Artificial neural networks (the multilayer perceptron): a review of applications in the atmospheric sciences.
Gibson, R., & Schwartz, E. S. (1990). Stochastic convenience yield and the pricing of oil contingent claims.
Golyandina, N., Nekrutkin, V., & Zhigljavsky, A. A. (2001).
Hassani, H. (2007). Singular spectrum analysis: methodology and comparison.
Hassani, H., Heravi, S., & Zhigljavsky, A. (2009). Forecasting European industrial production with singular spectrum analysis.
Hassani, H., Webster, A., Silva, E. S., & Heravi, S. (2015). Forecasting U.S. tourist arrivals using optimal singular spectrum analysis.
Huang, G.-B., Zhu, Q.-Y., & Siew, C.-K. (2006). Extreme learning machine: theory and applications.
Huang, X., Maçaira, P. M., Hassani, H., Cyrino Oliveira, F. L., & Dhesi, G. (2019). Hydrological natural inflow and climate variables: time and frequency causality analysis.
Hyndman, R. J., & Athanasopoulos, G. (2021).
Hyndman, R. J., & Koehler, A. B. (2006). Another look at measures of forecast accuracy.
Hyndman, R., Athanasopoulos, G., Bergmeir, C., Caceres, G., Chhay, L., O’Hara-Wild, M., Petropoulos, F., Razbash, S., Wang, E., Yasmeen, F., Garza, F., Girolimetto, D., Ihaka, R., Reid, D., Shaub, D., Tang, Y., Wang, X., & Zhou, Z. (2022).
Kavzoglu, T., & Mather, P. M. (2003). The use of backpropagating artificial neural networks in land cover classification.
Kwiatkowski, D., Phillips, P. C. B., Schmidt, P., & Shin, Y. (1992). Testing the null hypothesis of stationarity against the alternative of a unit root: How sure are we that economic time series have a unit root?
Li, J., Zhu, S., & Wu, Q. (2019). Monthly crude oil spot price forecasting using variational mode decomposition.
Lima, F. G., Kimura, H., Assaf Neto, A., & Perera, L. C. J. (2010). Previsão de preços de commodities com modelos ARIMA-GARCH e redes neurais com ondaletas: velhas tecnologias - novos resultados.
Liu, K., Cheng, J., & Yi, J. (2022). Copper price forecasted by hybrid neural network with Bayesian Optimization and wavelet transform.
Maia, A. L. S., Carvalho, F. A. T., & Ludermir, T. B. (2008). Forecasting models for interval-valued time series.
Meira, E., Cyrino Oliveira, F. L., & de Menezes, L. M. (2021). Point and interval forecasting of electricity supply via pruned ensembles.
Meira, E., Cyrino Oliveira, F. L., & de Menezes, L. M. (2022). Forecasting natural gas consumption using Bagging and modified regularization techniques.
Paul, R. K., & Garai, S. (2021). Performance comparison of wavelets-based machine learning technique for forecasting agricultural commodity prices.
Phillips, P. C. B., & Perron, P. (1988). Testing for a unit root in time series regression.
Ribeiro, C. O., & Oliveira, S. M. (2011). A hybrid commodity price-forecasting model applied to the sugar-alcohol sector.
Rodrigues, P. C., Tuy, P. G. S. E., & Mahmoudvand, R. (2018). Randomized singular spectrum analysis for long time series.
Safari, A., & Davallou, M. (2018). Oil price forecasting using a hybrid model.
Sanei, S., & Hassani, H. (2015).
Schwartz, E. S. (1997). The stochastic behavior of commodity prices: Implications for valuation and hedging.
Sugiura, N. (1978). Further analysis of the data by Akaike s information criterion and the finite corrections.
Universidade de São Paulo - USP, Centro de Estudos Avançados em Economia Aplicada - CEPEA. (2021).
Universidade de São Paulo - USP, Centro de Estudos Avançados em Economia Aplicada - CEPEA. (2022).
Wang, D., Yue, C., Wei, S., & Lv, J. (2017). Performance analysis of four decomposition-ensemble models for one-day-ahead agricultural commodity futures price forecasting.
Wang, J., & Li, X. (2018). A combined neural network model for commodity price forecasting with SSA.
Xiong, T., Li, C., & Bao, Y. (2018). Seasonal forecasting of agricultural commodity price using a hybrid STL and ELM method: evidence from the vegetable market in China.
Xiong, T., Li, C., Bao, Y., Hu, Z., & Zhang, L. (2015). A combination method for interval forecasting of agricultural commodity futures prices.
Zhang, W., Su, Z., Zhang, H., Zhao, Y., & Zhao, Z. (2014). Hybrid wind speed forecasting model study based on SSA and intelligent optimized algorithm.
Submitted date:
02/25/2022
Accepted date:
09/22/2022