An innovative machine learning algorithm-based approach to financial forecasting for business management

Abstract

A crucial component of company management is financial forecasting, which enables firms to deploy resources wisely and make educated decisions. Traditional forecasting techniques frequently rely on statistical models and historical data, which could not adequately account for the complexity of contemporary financial markets. This study uses cutting-edge machine learning algorithms to provide a novel method for financial forecasting. This study proposes a strategy for improving the precision and stability of financial forecasts by harnessing the power of machine learning and financial data. We have gathered data and utilized Z-score Normalization for pre-processing. Next, we extracted the attributes using, Principal Component Analysis (PCA). We presented the Adaptive Runge Kutta Optimized Weighted long Short Term Memory (ARKO-WLSTM) to financial forecasting for business management. The research has shown that the long short term memory approach may be applied to the finance prediction successfully. The suggested approach improves the Accuracy, precision, recall and F-score as compared with the current methods.