Published Paper Details:

Businesses must optimize their sales income in order to be profitable in ever-changing markets. Demand elasticity, competitive pricing, and real-time market variations are not taken into account by traditional approaches like cost-based pricing, rule-based pricing, and basic regression models, which results in less-than-ideal revenue outcomes in this research, we enhance pricing tactics by combining cutting-edge mathematics and machine learning approaches. The price-demand connection is modelled using quadratic regression, and high-accuracy demand forecasting is achieved by integrating long-term sequence learning (LSTM) with short-term trend analysis (ARIMA) in ARIMA-LSTM hybrid models. Our approach uses reinforcement learning (Q-learning) and game theory (Nash Equilibrium) to dynamically modify pricing in response to real-time changes in demand and competitive activities. Higher revenue optimization, improved demand forecasting, and improved competitive positioning are all made possible by these techniques, which also allow for clever pricing schemes that adjust to customer behaviour and market developments.