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Seq2Seq RNNs and ARIMA models for Cryptocurrency Prediction: A Comparative Study
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
2018 (English)In: Proceedings of SIGKDD Workshop on Fintech (SIGKDD Fintech’18), 2018, article id 4Conference paper, Published paper (Refereed)
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.

Place, publisher, year, edition, pages
2018. article id 4
Keywords [en]
Neural Networks, Machine Learning, ARIMA, Cryptocurrency
National Category
Computer Sciences
Research subject
Computer and Systems Sciences
Identifiers
URN: urn:nbn:se:su:diva-161409DOI: 10.475/123_4OAI: oai:DiVA.org:su-161409DiVA, id: diva2:1258222
Conference
SIGKDD Fintech’18, London, UK, August 19-23, 2018
Available from: 2018-10-24 Created: 2018-10-24 Last updated: 2019-04-16Bibliographically approved

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Rebane, JonathanKarlsson, IsakPapapetrou, Panagiotis
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