Steam players count prediction using GRU optimized by modified PSO

Accurate prediction of Steam players count is vital for efficient resource management, informed decision-making, and ability to maximize the potential of each game over its entire lifecycle. From the business point of view, this can directly affect profitability, player retention and strategic planning. This paper examines potential of GRU tuned by an improved version of renowned particle swarm optimization algorithm. The proposed model was placed into comparative simulations with other potent optimization techniques, and obtained experimental outcomes clearly pointed out the supreme performance of the suggested technique.