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Financial time series prediction using distributed machine learning techniques

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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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Authors and Affiliations

  1. Department of CSIT, Siksha ‘O’ Anusandhan University, Bhubaneswar, Odisha, India

    Usha Manasi Mohapatra

  2. Department of CSIT, Guru Ghasidas Vishwavidyalaya (Central University), Bilaspur, India

    Babita Majhi

  3. Department of CSE, PVP Siddhartha Institute of Technology, Vijayawada, AP, India

    Suresh Chandra Satapathy

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  1. Usha Manasi Mohapatra
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  2. Babita Majhi
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  3. Suresh Chandra Satapathy
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Correspondence to Usha Manasi Mohapatra.

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Mohapatra, U.M., Majhi, B. & Satapathy, S.C. Financial time series prediction using distributed machine learning techniques. Neural Comput & Applic 31, 3369–3384 (2019). https://doi.org/10.1007/s00521-017-3283-2

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  • DOI: https://doi.org/10.1007/s00521-017-3283-2

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