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Multiscale Agricultural Commodities Forecasting Using Wavelet-SARIMA Process

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Abstract

Forecasts of spot or future prices for agricultural commodities make it possible to anticipate the favorable or above all unfavorable development of future profits from the exploitation of agricultural farms or agri-food enterprises. Previous research has shown that cyclical behavior is a dominant feature of the time series of prices of certain agricultural commodities, which may be affected by a seasonal component. Wavelet analysis makes it possible to capture this cyclicity by decomposing a time series into its frequency and time domains. This paper proposes a time-frequency decomposition based approach to choose a seasonal auto-regressive aggregate (SARIMA) model for forecasting the monthly prices of certain agricultural futures prices. The originality of the proposed approach is due to the identification of the optimal combination of the wavelet transformation type, the wavelet function and the number of decomposition levels used in the multi-resolution approach (MRA), that significantly increase the accuracy of the forecast. Our SARIMA hybrid approach contributes to take into account the cyclicity and of the seasonality when predicting commodity prices. As a relevant result, our study allows an economic agent, according to his forecasting horizon, to choose according to the available data, a specific SARIMA process for forecasting.

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Notes

  1. Yves Meyer is a Emeritus Professor at Superior Normal School of Cachan, Member of the Academic of Sciences since 1993. Specialist of harmonic analysis, he discovered the orthogonal wavelets.

  2. \(L^2(R)\) is the set of square integrable functions: \(\int _{-\infty }^{+\infty } \left| f(t)\right| ^2dt < +\infty\) and a Hilbert’s space for the scalar product \(\left\langle f, \psi _{u,s} \right\rangle\).

  3. The data time series are available on https://www.quandl.com.

  4. symmetric distribution and a flattened like Gauss’s.

  5. graphics of standardised residual, graphic of simple and partial autocorrelation function and graphic of Ljung-Box test.

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Authors and Affiliations

  1. MRE UR 209 et faculté d’Économie, Université de Montpellier, Montpellier, France

    Mamadou-Diéne Diop & Jules Sadefo Kamdem

  2. MRE UR MRE UR 209, (Université de Montpellier), Faculté d’Economie Avenue Raymond Dugrand, Site de Richter C.S. 79606, 34960, Montpellier Cedex 2, France

    Jules Sadefo Kamdem

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  1. Mamadou-Diéne Diop
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Correspondence to Jules Sadefo Kamdem.

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Appendix

Appendix

See Figs. 11, 12, 13, 14, 15, 16, 17, 18, 19, 20.

Fig. 11
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Annual average data series

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Monthly average indices prices

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Residuals diagnostics by Seasonal Arima model for cereals data series

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Residuals diagnostics by Seasonal Arima model for oleaginous data series

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MRA on Soy times series

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Fig. 16
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MRA on LogSoy times series

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Fig. 17
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Wavelet Simple autocorrelation function - Wheat

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Wavelet Partial autocorrelation function - Wheat

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Fig. 19
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Wavelet Simple autocorrelation function - Soy

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Fig. 20
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Wavelet Partial autocorrelation function - Soy

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Diop, MD., Sadefo Kamdem, J. Multiscale Agricultural Commodities Forecasting Using Wavelet-SARIMA Process. J. Quant. Econ. 21, 1–40 (2023). https://doi.org/10.1007/s40953-022-00329-4

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