1 research outputs found
Hierarchical Ensemble-Based Feature Selection for Time Series Forecasting
We study a novel ensemble approach for feature selection based on
hierarchical stacking in cases of non-stationarity and limited number of
samples with large number of features. Our approach exploits the co-dependency
between features using a hierarchical structure. Initially, a machine learning
model is trained using a subset of features, and then the model's output is
updated using another algorithm with the remaining features to minimize the
target loss. This hierarchical structure allows for flexible depth and feature
selection. By exploiting feature co-dependency hierarchically, our proposed
approach overcomes the limitations of traditional feature selection methods and
feature importance scores. The effectiveness of the approach is demonstrated on
synthetic and real-life datasets, indicating improved performance with
scalability and stability compared to the traditional methods and
state-of-the-art approaches