Please use this identifier to cite or link to this item: https://gnanaganga.inflibnet.ac.in:8443/jspui/handle/123456789/15697
Full metadata record
DC FieldValueLanguage
dc.contributor.authorKumar, Amit-
dc.contributor.authorSharma, Neha-
dc.contributor.authorGurna, Kamalpreet Kaur-
dc.contributor.authorAnand, Abhineet-
dc.contributor.authorPatni, Jagdish Chandra-
dc.contributor.authorPinjarkar, Latika-
dc.date.accessioned2024-05-29T08:53:05Z-
dc.date.available2024-05-29T08:53:05Z-
dc.date.issued2024-
dc.identifier.citationVol. 5, No. 6; pp. 273-288en_US
dc.identifier.issn2633-352X-
dc.identifier.urihttp://dx.doi.org/10.61707/7074ja52-
dc.identifier.urihttp://gnanaganga.inflibnet.ac.in:8080/jspui/handle/123456789/15697-
dc.description.abstractThe 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 re sults 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 havi ng to cope with the difficulties of crypto-currency markets. © 2024, Transnational Press London Ltd. All rights reserved.en_US
dc.language.isoenen_US
dc.publisherInternational Journal of Religionen_US
dc.publisherTransnational Press London Ltden_US
dc.subjectArima Analysisen_US
dc.subjectCryptocurrencyen_US
dc.subjectExploratory Data Analysisen_US
dc.subjectInterquartile Rangeen_US
dc.subjectStellar Xlmen_US
dc.titleForecasting Stellar Xlm Prices: Insights From Arima Analysisen_US
dc.typeArticleen_US
Appears in Collections:Journal Articles

Files in This Item:
File SizeFormat 
IJOR-024-19355(6)273-288.pdf529.62 kBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.