BootBoGs-TS: Bootstrap-Based Hyperparameter Optimization for LSTM Models in Remaining Useful Life Prediction
Article : Articles dans des revues internationales ou nationales avec comité de lecture
Neural networks, particularly Long Short-Term Memory (LSTM) models, have
become prominent for Remaining Useful Life (RUL) prediction due to their
ability to capture temporal degradation patterns from multivariate sensor data.
However, their predictive performance is highly sensitive to hyperparameter
configurations, and conventional optimization methods such as Grid Search or
Bayesian Optimization often become inefficient or unstable when applied to highdimensional,
sequential data. To address this, we introduce BootBoGs-TS, a
hybrid hyperparameter optimization framework for LSTM-based RUL prediction
from time series. BootBoGs-TS extends our previously proposed BootBoGs
algorithm, originally developed for classification tasks, by integrating a unitlevel
Bootstrap that resamples complete engine trajectories to preserve long-term
degradation integrity with an out-of-bag (OOB) evaluation scheme, and introducing
a performance-based filtering step. This filtering step retains only stable
configurations whose validation scores lie within a median ± k·MAD interval,
where MAD denotes the median absolute deviation computed from the empirical
distribution of OOB S-scores. This procedure discards unstable or extreme
hyperparameter settings and thereby reduces the search space in a statistically
principled manner. The proposed framework sequentially combines Bayesian
Optimization to define the initial hyperparameter ranges, bootstrap to explore
variability in model performance while preserving temporal dependencies, and
reduced-grid search for final refinement. We evaluate BootBoGs-TS on the CMAPSS
benchmark dataset. Experiments are conducted on the four C-MAPSS
subsets (FD001–FD004). Detailed comparative experiments and ablation studies are performed on the FD004 subset, which represents the most complex scenario.
Results show that BootBoGs-TS improves both efficiency and stability, achieving
lower RMSE and S-score values compared to standard baselines.