  In predictive modeling, overfitting poses a significant risk, particularly
when the feature count surpasses the number of observations, a common scenario
in high-dimensional data sets. To mitigate this risk, feature selection is
employed to enhance model generalizability by reducing the dimensionality of
the data. This study focuses on evaluating the stability of feature selection
techniques with respect to varying data volumes, particularly employing time
series similarity methods. Utilizing a comprehensive dataset that includes the
closing, opening, high, and low prices of stocks from 100 high-income companies
listed in the Fortune Global 500, this research compares several feature
selection methods including variance thresholds, edit distance, and Hausdorff
distance metrics. The aim is to identify methods that show minimal sensitivity
to the quantity of data, ensuring robustness and reliability in predictions,
which is crucial for financial forecasting. Results indicate that among the
tested feature selection strategies, the variance method, edit distance, and
Hausdorff methods exhibit the least sensitivity to changes in data volume.
These methods therefore provide a dependable approach to reducing feature space
without significantly compromising the predictive accuracy. This study not only
highlights the effectiveness of time series similarity methods in feature
selection but also underlines their potential in applications involving
fluctuating datasets, such as financial markets or dynamic economic conditions.
The findings advocate for their use as principal methods for robust feature
selection in predictive analytics frameworks.
