Prediction of long memory time series with time variability

Haotian Guan; Jing Li

doi:10.1117/12.2635912

6 May 2022 Prediction of long memory time series with time variability

Haotian Guan, Jing Li

Proceedings Volume 12256, International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2022); 122562G (2022) https://doi.org/10.1117/12.2635912
Event: 2022 International Conference on Electronic Information Engineering, Big Data and Computer Technology, 2022, Sanya, China

Abstract

No matter in financial theory research or financial practice application, the long memory time series problem is of great significance. The ARFIMA model is one of the commonly used long-memory financial models. It has problems such as the lag of the long-memory relationship caused by the untimely update of fractional parameters, and the large deviation of parameter values caused by a small number of data sub-intervals. Based on this, this paper proposes a new method with a time-varying Hurst exponent. The new definition of fractional calculus is used to combine with the long memory model ARFIMA to explore the long memory relationship of Chinese financial products and predict the changing trend of financial products. The improved method can effectively solve the problem of interval selection of the R/S analysis method, and the Hurst exponent is also more in line with the changing characteristics of time series. The experimental results show that the new method improves the model performance, better fits the forecast trend curve and effectively reduces the forecast error of financial products. It provides a new research perspective for the non-linearity and complexity of the current financial market, and the empirical results show that the method has practical significance.

Citation Download Citation

Haotian Guan and Jing Li "Prediction of long memory time series with time variability", Proc. SPIE 12256, International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2022), 122562G (6 May 2022); https://doi.org/10.1117/12.2635912

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