Comparative Analysis of Newton's Interpolation and Lagrange Interpolation for Financial Forecasting

Financial forecasting is a critical tool in economic decision-making, helping businesses and investors predict market trends and allocate resources efficiently. Traditional forecasting models often require extensive dataset and struggle with short-term predictions in volatile market. This study explores the application of Newton's interpolation and LaGrange interpolation as alternative methods for financial forecasting. Using historical stock price data from Tesla Inc (TSLA), we implemented both interpolation techniques to predict future stock prices. The results indicate that Newton's interpolation provides a more accurate forecast compared to LaGrange interpolation, with a smaller error margin. The findings highlight the effectiveness of Newton's method in short-term financial forecasting, while also acknowledging the limitation of interpolation techniques in handling market volatility. The study leads in the recommendation of integrating interpolation with statistical and technical analysis methods to enhance predictive accuracy.