FateMurphy/CEEMDAN_LSTM
CEEMDAN_LSTM is a Python project for decomposition-integration forecasting models based on EMD methods and LSTM.
Combines CEEMDAN signal decomposition with multi-layer LSTM networks to isolate high-frequency and low-frequency components for separate forecasting, then integrates predictions using configurable strategies (ensemble, respective, hybrid). Supports multiple decomposition modes (EMD, EEMD, VMD, OVMD, SVMD) and re-decomposition of individual IMFs, with optional re-normalization and automated parameter tuning via Keras/TensorFlow, including built-in statistical validation (ADF, Ljung-Box tests) and comparative analysis (Diebold-Mariano test).
292 stars and 139 monthly downloads. No commits in the last 6 months. Available on PyPI.
Stars
292
Forks
49
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Mar 03, 2025
Monthly downloads
139
Commits (30d)
0
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