Time Varying Pricing Tariff

Time-varying pricing tariffs dynamically adjust prices to incentivize efficient resource allocation, primarily seen in energy markets and insurance. Research focuses on developing accurate predictive models, often employing machine learning techniques like neural networks (including recurrent and feed-forward architectures) and gradient boosting, to forecast consumer response and optimize tariff design for fairness and revenue maximization. These models aim to balance consumer affordability with efficient resource use, impacting both economic efficiency and the design of equitable pricing strategies across various sectors.