Mahdi Goldani; Soraya Asadi Tirvan
Abstract
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 ...
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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.
Mahdi Goldani
Abstract
Forecasting in the financial markets is vital for informed decision-making, risk management, efficient capital allocation, asset valuation, and economic stability. This study thoroughly examines forecasting techniques to predict the 30-day closing prices of APPLE in a select group of 100 prominent companies ...
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Forecasting in the financial markets is vital for informed decision-making, risk management, efficient capital allocation, asset valuation, and economic stability. This study thoroughly examines forecasting techniques to predict the 30-day closing prices of APPLE in a select group of 100 prominent companies chosen based on their revenue profiles. list of 100 big Companies published by The Fortune Global 500. The evaluated forecasting methods encompass a broad spectrum of approaches, including Moving Average (MA), Exponential Smoothing, Autoregressive Integrated Moving Average (ARIMA), Simple Linear Regression, Multiple Regression, Decision Trees, Random Forests, Neural Networks, and Support Vector Regression (SVR). The information on the dataset was downloaded from Yahoo Finance, and all methods were evaluated in Python. The MAPE method is used to measure the accuracy of the examined methods. Based on the selected dataset, Our findings reveal that SVR, Simple Linear Regression, Neural Networks, and ARIMA consistently outperform other methods in accurately predicting the 30-day APPLE closing prices. In contrast, the Moving Average method exhibits subpar performance, primarily due to its inherent limitations in accommodating the intricate dynamics of financial data, such as trends, seasonality, and unexpected shocks. In conclusion, this comprehensive analysis enhances our understanding of forecasting techniques and paves the way for more informed and precise decision-making in the ever-evolving realm of financial markets.
