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Day ahead forecasting of FAANG stocks using ARIMA, LSTM networks and wavelets.

Skehin, Tom, Crane, Martin orcid logoORCID: 0000-0001-7598-3126 and Bezbradica, Marija (2018) Day ahead forecasting of FAANG stocks using ARIMA, LSTM networks and wavelets. In: The 26th AIAI Irish Conference on Artificial Intelligence and Cognitive Science, 6-7 Dec 2018, Dublin, Ireland.

Abstract
Abstract. Facebook Inc., Apple Inc., Amazon.com Inc., Net ix Inc. and Alphabet Inc., known collectively as FAANG, are a group of the best performing tech stocks in recent years. In this study, we present linear and non-linear methods for predicting the closing price of each stock on the following day. We decompose each time series into component series using wavelet methods and develop an novel ensemble approach to improve forecast accuracy.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Uncontrolled Keywords:Time Series modelsl; ARIMA; Wavelets; LSTM
Subjects:Computer Science > Artificial intelligence
Computer Science > Computer simulation
Computer Science > Machine learning
Mathematics > Economics, Mathematical
Mathematics > Mathematical models
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Initiatives and Centres > Scientific Computing and Complex Systems Modelling (Sci-Sym)
Research Initiatives and Centres > ADAPT
Published in: Proceedings of The 26th AIAI Irish Conference on Artificial Intelligence and Cognitive Science. 2259. CEUR Workshop Proceedings.
Publisher:CEUR Workshop Proceedings
Official URL:http://http//ceur-ws.org/Vol-2259/
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:ADAPT
ID Code:22849
Deposited On:19 Dec 2018 12:26 by Martin Crane . Last Modified 17 Apr 2019 08:34
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