Steam Players Count Forecasting with LSTM Tuned by Modified RSA

Precise estimation of the number of Steam users is crucial for optimal resource allocation, well-informed strategic choices, and the capability to fully leverage a game's potential throughout its lifespan. From a commercial perspective, this aspect significantly influences revenue generation, player engagement, and long-term planning. From the players' perspective, it allows server stability by anticipating peak loads and avoiding potential congestion, more balanced player matchmaking and tracking game's popularity trends. This study explores the effectiveness of an LSTM model refined through an enhanced variation of the highly acclaimed reptile search optimization algorithm. The developed approach was subjected to benchmarking simulations alongside other robust optimization methods, with the experimental findings distinctly highlighting the superior efficiency of the proposed technique. Ultimately, the proposed approach achieved the lowest MSE of 0.030559 and highest R2 of 0.430862 among all methods evaluated side by side comparisons.