Sensitivity Assessing to Data Volume for forecasting: introducing similarity methods as a suitable one in Feature selection methods

Abstract

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.