| Name : R-CEEMDANML
| |
| Version : 0.1.0
| Vendor : obs://build_opensuse_org/devel:languages:R
|
| Release : lp153.2.3
| Date : 2024-06-14 11:37:19
|
| Group : Development/Libraries/Other
| Source RPM : R-CEEMDANML-0.1.0-lp153.2.3.src.rpm
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| Size : 0.05 MB
| |
| Packager : (none)
| |
| Summary : CEEMDAN Decomposition Based Hybrid Machine Learning Models
|
Description :
Noise in the time-series data significantly affects the accuracy of the
Machine Learning (ML) models (Artificial Neural Network and Support
Vector Regression are considered here). Complete Ensemble Empirical
Mode Decomposition with Adaptive Noise (CEEMDAN) decomposes the time
series data into sub-series and help to improve the model performance.
The models can achieve higher prediction accuracy than the traditional
ML models. Two models have been provided here for time series
forecasting. More information may be obtained from Garai and Paul
(2023) < doi:10.1016/j.iswa.2023.200202>.
|
RPM found in directory: /packages/linux-pbone/ftp5.gwdg.de/pub/opensuse/repositories/devel:/languages:/R:/autoCRAN/openSUSE_Leap_15.3/x86_64 |
