TY - GEN
T1 - Economic index forecasting via multi-scale recursive dynamic factor analysis
AU - Yuan, Haoyi
AU - Yuan, Yang
AU - Wu, H. C.
AU - Zou, Yuexian
N1 - Publisher Copyright:
© Springer International Publishing AG, part of Springer Nature 2018.
PY - 2018
Y1 - 2018
N2 - In this paper, we propose a new multi-scale recursive dynamic factor analysis (MS-RDFA) algorithm for economic index foresting (EIF). The proposed MS-RDFA algorithm first employ empirical mode decomposition (EMD), which is a powerful tool for multi-scale analysis and modeling on the non-linear and non-stationary signal such as economic index data. Moreover, an efficient RDFA algorithm using recursive subspace tracking is adopted to explore the correlated nature of the adjacent intervals of the economic index data. The one-step prediction of PC scores is modeled as an AR process and can be recursively tracked by Kalman filter (KF). The major advantage of the proposed MS-RDFA method is its low arithmetic complexity and simple real-time updating, which is different from other conventional algorithms. This makes it as an attractive alternative to other conventional approaches to EIF on mobile services. The experiments show that the proposed MS-RDFA algorithm has better forecasting results than other EIF methods.
AB - In this paper, we propose a new multi-scale recursive dynamic factor analysis (MS-RDFA) algorithm for economic index foresting (EIF). The proposed MS-RDFA algorithm first employ empirical mode decomposition (EMD), which is a powerful tool for multi-scale analysis and modeling on the non-linear and non-stationary signal such as economic index data. Moreover, an efficient RDFA algorithm using recursive subspace tracking is adopted to explore the correlated nature of the adjacent intervals of the economic index data. The one-step prediction of PC scores is modeled as an AR process and can be recursively tracked by Kalman filter (KF). The major advantage of the proposed MS-RDFA method is its low arithmetic complexity and simple real-time updating, which is different from other conventional algorithms. This makes it as an attractive alternative to other conventional approaches to EIF on mobile services. The experiments show that the proposed MS-RDFA algorithm has better forecasting results than other EIF methods.
KW - Economic index forecasting
KW - Empirical mode decomposition (EMD)
KW - Recursive dynamic factor analysis (RDFA)
UR - https://www.scopus.com/pages/publications/85049363589
U2 - 10.1007/978-3-319-94361-9_7
DO - 10.1007/978-3-319-94361-9_7
M3 - Conference contribution
AN - SCOPUS:85049363589
SN - 9783319943602
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 83
EP - 91
BT - Artificial Intelligence and Mobile Services – AIMS 2018 - 7th International Conference, Held as Part of the Services Conference Federation, SCF 2018, Proceedings
A2 - Yang, Yujiu
A2 - Zhang, Liang-Jie
A2 - Zou, Yuexian
A2 - Aiello, Marco
T2 - 7th International Conference on Artificial Intelligence and Mobile Services, AIMS 2018 Held as Part of the Services Conference Federation, SCF 2018
Y2 - 25 June 2018 through 30 June 2018
ER -