De-noising classification method for financial time series based on ICEEMDAN and wavelet threshold, and its application
Abstract This paper proposes a classification method for financial time series that addresses the significant issue of noise. The proposed method combines improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and wavelet threshold de-noising. The method begins by emp...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2024-01-01
|
| Series: | EURASIP Journal on Advances in Signal Processing |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13634-024-01115-5 |