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Seq2Seq RNNs and ARIMA models for Cryptocurrency Prediction: A Comparative Study
Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.ORCID-id: 0000-0002-4632-4815
2018 (engelsk)Inngår i: Proceedings of SIGKDD Workshop on Fintech (SIGKDD Fintech’18), 2018, artikkel-id 4Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Cyrptocurrency price prediction has recently become an alluring topic, attracting massive media and investor interest. Traditional models, such as Autoregressive Integrated Moving Average models (ARIMA) and models with more modern popularity, such as Recurrent Neural Networks (RNN’s) can be considered candidates for such financial prediction problems, with RNN’s being capable of utilizing various endogenous and exogenous input sources. This study compares the model performance of ARIMA to that of a seq2seq recurrent deep multi-layer neural network (seq2seq) utilizing a varied selection of inputs types. The results demonstrate superior performance of seq2seq over ARIMA, for models generated throughout most of bitcoin price history, with additional data sources leading to better performance during less volatile price periods.

sted, utgiver, år, opplag, sider
2018. artikkel-id 4
Emneord [en]
Neural Networks, Machine Learning, ARIMA, Cryptocurrency
HSV kategori
Forskningsprogram
data- och systemvetenskap
Identifikatorer
URN: urn:nbn:se:su:diva-161409OAI: oai:DiVA.org:su-161409DiVA, id: diva2:1258222
Konferanse
SIGKDD Fintech’18, London, UK, August 19-23, 2018
Tilgjengelig fra: 2018-10-24 Laget: 2018-10-24 Sist oppdatert: 2019-12-17bibliografisk kontrollert

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