sensitivity assessing to data volume for forecasting: introducing similarity methods as suitable ones in feature selection methods

In predictive modeling, overfitting poses a significant risk, particularly when the feature count surpasses the number of observations, a common scenario in highdimensional datasets. to mitigate this risk, feature selection is employed to enhance model generalizability by reducing the dimensionality of the data. this study evaluates the stability of feature selection techniques with respect to varying data volumes, focusing on 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. numerous feature selection methods were investigated in literature. selecting the more accurate feature selection methods in order to forecast can be challenging [1]. so, this study examines the most well-known feature selection methods’ performance in different data sizes. 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 predictive accuracy. this study highlights the effectiveness of time series similarity methods in feature selection and underlines their potential in applications involving fluctuating datasets, such as financial markets or dynamic economic conditions.