Forecasting Stellar XLM Prices: Insights from ARIMA Analysis

Abstract

The given research is aimed at solving the urgent problem of verifiable methods of forecasting cryptocurrency trading, with a preset focus on Stellar (XLM). Nevertheless, cryptocurrency forecasting is becoming more and more popular and it is still the case that there is a deficiency of research devoted to the use of the most advanced models to Stellar XLM price data. In many instances, the existing studies may pay less attention to the specific features of this cryptocurrency, hence creating a gap between our knowledge and understanding of how its prices fluctuate. Our experimental approach will investigate what accuracy forecasting models, especially the ARIMA model, can come up with by predicting the price of Stellar XLM. The purpose of this research is to experiment with a dataset for several years to know the workability of theoretical results on the forecasting models of Stellar XLM cryptocurrency. Our experimental research evidenced the acceptable price accuracy when forecasting Stellar XLM prices. Regarding the volume data, our metrics are a MAPE (Mean Absolute Percentage Error) of 16.82% and MSE (Mean Squared Error) of 7.41.10 -15 and Accuracyman (Accuracy) of 83.18%. On the other hand, the data with very high data recorded the best performance, with a MAPE of 4. 26%, MSE = 0.00025, and Accuracy of 95.74%. This finding again evinces that applying the more advanced models of forecasting and choosing appropriate data sources is key in a good Stellar XLM forecast. Thereby this study contributes to filling the research gaps and by administrative tools providing insights into the practical use of forecasting models, it guides stakeholders having to cope with the difficulties of crypto-currency markets.