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Erschienen in: Neural Computing and Applications 8/2019

21.11.2017 | Original Article

Financial time series prediction using distributed machine learning techniques

verfasst von: Usha Manasi Mohapatra, Babita Majhi, Suresh Chandra Satapathy

Erschienen in: Neural Computing and Applications | Ausgabe 8/2019

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Abstract

The financial time series is inherently nonlinear and hence cannot be efficiently predicted by using linear statistical methods such as regression. Hence, intelligent predictor has been developed and reported which is suitable for nonlinear time series. But such predictors require that the past financial data are available at the location of the predictor which is not the case in many real-life situations. Hence, when the financial data are available at different places and a single intelligent predictor needs to be developed, the task becomes challenging. In the current work, this problem has been addressed and solved using a low-complexity artificial neural network and employing incremental and diffusion learning strategies. In the current study, distributed prediction of three different types of time series such as exchange rates, stock indices and net asset values has been carried using incremental and diffusion-based learning strategies. The results of different days ahead prediction of two proposed low computational complexity-based functional link artificial neural network are compared with those obtained by conventional intelligent method. The results of simulation-based experiments reveal similar or improved prediction performance of the proposed distributed predictors compared to conventional one. In addition, saving in band width, memory and power are achieved in this method.

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Metadaten
Titel
Financial time series prediction using distributed machine learning techniques
verfasst von
Usha Manasi Mohapatra
Babita Majhi
Suresh Chandra Satapathy
Publikationsdatum
21.11.2017
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 8/2019
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-017-3283-2

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