Financial Time Series Prediction Using Spiking Neural Networks
- Submitting institution
-
Liverpool Hope University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- DR12C
- Type
- D - Journal article
- DOI
-
10.1371/journal.pone.0103656
- Title of journal
- PLoS ONE
- Article number
- -
- First page
- e103656
- Volume
- 9
- Issue
- 8
- ISSN
- 1932-6203
- Open access status
- Compliant
- Month of publication
- -
- Year of publication
- 2014
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- Yes
- Number of additional authors
-
2
- Research group(s)
-
I - Intelligent and Distributed Systems (IDS)
- Citation count
- 6
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- In this paper, a novel application of a particular type of spiking neural network, a Polychronous Spiking Network, is used for financial time series prediction. This was one of the first papers that used this emerging area of neural networks research for financial prediction. It generated much interest in the FinTech community and led to discussions with the CEO of a hardware manufacturer of Spiking Neural Networks (BrainChip).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
