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dc.contributor.authorChhem, Sronglong
dc.contributor.authorAnjum, Ashiq
dc.contributor.authorArshad, Bilal
dc.date.accessioned2020-12-04T15:56:42Z
dc.date.available2020-12-04T15:56:42Z
dc.date.issued2019-12
dc.identifier.citationChhem, S., Anjum, A. and Arshad, B., (2019). 'Intelligent Price Alert System for Digital Assets-Cryptocurrencies'. In Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 109-115.en_US
dc.identifier.issn9781450370448
dc.identifier.doi10.1145/3368235.3368874
dc.identifier.urihttp://hdl.handle.net/10545/625448
dc.description.abstractCryptocurrency market is very volatile, trading prices for some tokens can experience a sudden spike up or downturn in a matter of minutes. As a result, traders are facing difficulty following with all the trading price movements unless they are monitoring them manually. Hence, we propose a real-time alert system for monitoring those trading prices, sending notifications to users if any target prices match or an anomaly occurs. We adopt a streaming platform as the backbone of our system. It can handle thousands of messages per second with low latency rate at an average of 19 seconds on our testing environment. Long-Short-Term-Memory (LSTM) model is used as an anomaly detector. We compare the impact of five different data normalisation approaches with LSTM model on Bitcoin price dataset. The result shows that decimal scaling produces only Mean Absolute Percentage Error (MAPE) of 8.4 per cent prediction error rate on daily price data, which is the best performance achieved compared to other observed methods. However, with one-minute price dataset, our model produces higher prediction error making it impractical to distinguish between normal and anomaly points of price movement.en_US
dc.description.sponsorshipN/Aen_US
dc.language.isoenen_US
dc.publisherACM Pressen_US
dc.relation.urlhttps://dl.acm.org/doi/abs/10.1145/3368235.3368874en_US
dc.rights.urihttp://www.acm.org/publications/policies/copyright_policy#Background
dc.sourceProceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion - UCC '19 Companion
dc.subjectCryptocurrency marketen_US
dc.subjecttrading pricesen_US
dc.titleIntelligent price alert system for digital assets - cryptocurrenciesen_US
dc.typeMeetings and Proceedingsen_US
dc.contributor.departmentUniversity of Derbyen_US
dc.identifier.journalUCC '19 Companion: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companionen_US
dcterms.dateAccepted2019
dc.author.detailN/Aen_US


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