Hybrid Minkowski–Log–Cosh Loss Function for Robust LSTM Time-Series Forecasting
Date:
This project presents a Hybrid Minkowski–Log–Cosh (MLC) loss function designed to improve the robustness of LSTM models on noisy, outlier-contaminated time-series data.
By combining a Minkowski-style exponent with the smoothness of the Log-Cosh function, MLC addresses the limitations of traditional losses like MSE and MAE, offering better stability and outlier resistance.
Highlights
- Improved stability and accuracy under noise
- Matched standard loss functions in computational cost
- Outperformed MSE/MAE in long-range forecasting
This research led to a first-author publication in IEEE Access (Q1).
📄 Read the paper
💻 View code on GitHub
Citation (APA)
Simsoba, K.-A., Oscar, N., & Mageto, T. (2025). A Hybrid Minkowski-Log-Cosh Loss Function for Robust LSTM-Based Time Series Forecasting. IEEE Access, 13, 187307–187319. https://doi.org/10.1109/ACCESS.2025.3626795