Forecasting Multivariate Time Series with a Dynamic-System-Based Hybrid Model

Author:

Ganaeva DariaORCID,Golovkina AnnaORCID

Publisher

Springer Nature Switzerland

Reference24 articles.

1. Bongard, J., Lipson, H.: Automated reverse engineering of nonlinear dynamical systems. Proc. Natl. Acad. Sci. 104(24), 9943–9948 (2007)

2. Box, G.E.P., Jenkins, G.M., Reinsel, G.C., Ljung, G.M.: Time Series Analysis: Forecasting and Control. Wiley series in probability and statistics, Wiley, Hoboken. fifth edn. (2016)

3. Brunton, S.L., Proctor, J.L., Kutz, J.N.: Discovering governing equations from data by sparse identification of nonlinear dynamical systems. Proc. Natl. Acad. Sci. 113(15), 3932–3937 (2016)

4. Burden, R.L., Faires, J.D., Burden, A.M.: Numerical Analysis. Cengage Learning. Boston 10 edn. (2016)

5. Chang, Y.Y., Sun, F.Y., Wu, Y.H., Lin, S.D.: A memory-network based solution for multivariate time-series forecasting (2018)

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