Business analysis using Long-Short Term Memory (LSTM) networks: A deep learning approach

This study investigates how Long Short-Term Memory (LSTM) networks are used in business analytics. Time-series forecasting, and anomaly detection are two areas in which LSTM networks, a subset of deep learning, excel. This paper shows how LSTM is applied in real-world scenarios, including demand prediction, network security, and sales forecasting, using case studies and real-world examples. It draws attention to the necessity for interpretable models, moral issues, and continuing studies in the area. A data-driven era of revolutionary decision-making is anticipated for LSTM in business analytics, with greater complexity, model transparency, and multidisciplinary applications.