Skehin, Tom, Crane, Martin 
ORCID: 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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