Shanghai copper futures prices forecast based on optimal number selection of eigentriples for reconstruction step of singular spectrum analysis

Guiqiu Lin; Jianbao Chen

doi:10.1117/12.2646351

23 August 2022 Shanghai copper futures prices forecast based on optimal number selection of eigentriples for reconstruction step of singular spectrum analysis

Guiqiu Lin, Jianbao Chen

Author Affiliations +

Proceedings Volume 12330, International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2022); 123301N (2022) https://doi.org/10.1117/12.2646351
Event: International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2022), 2022, Huzhou, China

Abstract

Singular spectrum analysis (SSA) is a nonlinear time series research method. Traditionally, the number of eigentriples to be reconstructed in the reconstruction step of the SSA method is usually decided by a given contribution rate threshold. However, this approach has inevitable subjectivity, and it will generate additional error. In order to remedy this deficiency, we propose an optimal number selection way by minimizing in-sample data error. We calculate the mean absolute percentage error (MAPE) values corresponding to all optional numbers of eigentriples using in-sample data. Then, the eigentriples corresponding to the lowest MAPE value are used as the reconstruction eigentriples. Meanwhile, the prediction coefficients corresponding to the lowest MAPE value are used as final coefficients in the linear recursive prediction formula. Based on this, out-of-sample data is predicted. The improved SSA method, the classical SSA method, and the ARIMA-GARCH model are compared in terms of forecast accuracy and computational time, using time series data from the closing prices of Shanghai copper futures. The results show that the improved method outperforms the classical ARIMA-GARCH and SSA models in forecast accuracy.

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Guiqiu Lin and Jianbao Chen "Shanghai copper futures prices forecast based on optimal number selection of eigentriples for reconstruction step of singular spectrum analysis", Proc. SPIE 12330, International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2022), 123301N (23 August 2022); https://doi.org/10.1117/12.2646351

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KEYWORDS

Copper

Spectrum analysis

Data modeling

Principal component analysis

Interference (communication)

Matrices

Process modeling

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