Comparison of Time Series Models for Predicting Online Gaming Company Revenue

Online gaming industry is an area where the effects of any change can be seen in a very short time. Therefore, real-time analysis of revenues, analysis of the commercial performance of the developed content, and rapid monitoring of the revenue contributions of the improvements are essential. Therefore, financial forecasting is a crucial part of business plan which can help strategize how much and how quickly the company intend to grow. In financial forecasting of a given time series, revenue estimations for future will become important research in the industry. This research offers a detailed analysis of recent time series models and focused on both deep learning and statistical methods for time series forecasting on real-world revenue data. Results of the study are examined using one of the leading Finland based online gaming companies’ revenue data. In our experiments, we investigated various time series forecast techniques, such as SARIMA, Theta, Holt Winters, Prophet, Dense Neural Network (DNN), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), N-Beats and Ensemble models. The experimental evaluations illustrate that deep learning models can optimize the financial forecast operations. The result of the study provides insights to managers and analysts in determining the best model to adopt.