Deep Learning Models for MIR-4 Draco Token Exchange Value Forecasting

MIR4, is a play to earn game that uses Non-Fungible Tokens (NFT) and cryptocurrency- or in MIR4, Draco Tokens- as a reward. Draco is obtained through mining an in-game resource called Darksteel and is then traded to Wemix Wallet, where real-world money is obtained. Cryptocurrencies are volatile, which gives MIR4 players and traders a decision dilemma of when is the preferable time to buy, sell, or trade Draco Tokens. In this study we present deep learning models, specifically the Long-Short Term Memory (LSTM) neural network, and Neural Prophet (NP) time series machine learning models to forecast future Draco-token exchange value. Historical data of Draco-token value from Yahoo Finance is utilized as a univariate parameter for the analysis, model development, and the forecasting of the future Draco-token exchange values. Performance of formulated models are assessed and compared based on the following regression metrics: RMSE, MSE, MAE and MAPE. Experimental results indicated that the LSTM Neural Network yielded better forecast estimates with lower error than the Neural Prophet. Findings of the study showed that LSTM can be utilized as a tool for forecasting future Draco token exchange values. future research direction suggests improving prediction accuracy by incorporating other parameters such as MIR-4 players sentiments, newly added players, and google search interest over time.

Keywords: Deep learning, Measurement, Neural networks, Time series analysis, Predictive models, Internet, Nonfungible tokens. 

To cite:  Gomez, N. A. I., Lumacad, G. S., Saludes, I. L. R., Castino, P. A. A., & Ligtao, O. T. B. (2023, June). Deep Learning Models for MIR-4 Draco Token Exchange Value Forecasting. In 2023 International Conference in Advances in Power, Signal, and Information Technology (APSIT) (pp. 268-273). IEEE