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International Conference of the Sri Lanka Association for Artificial Intelligence

SLAAI-ICAI 2018: Artificial Intelligence pp 79–91Cite as

A Novel Hybrid Back Propagation Neural Network Approach for Time Series Forecasting Under the Volatility

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 890))

Abstract

An Artificial Neural Network (ANN) algorithms have been widely used in machine learning for pattern recognition, classifications and time series forecasting today; especially in financial applications with nonlinear and nonparametric modeling’s. The objective of this study is an attempt to develop a new hybrid forecasting approach based on back propagation neural network (BPN) and Geometric Brownian Motion (GBM) to handle random walk data patterns under the high volatility. The proposed methodology is successfully implemented in the Colombo Stock Exchange (CSE) Sri Lanka, the daily demands of the All Share Price Index (ASPI) price index from April 2009 to March 2017. The performances of the model are evaluated based on the best two forecast horizons of 75% and 85% training samples. According to the empirical results, 85% training samples have given highly accurate in their testing process. Furthermore, the results confirmed that the proposed hybrid methodology always gives the best performances under the high volatility forecasting compared to the separate traditional time series models.

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Acknowledgments

This work was supported by the Research Grant (SUSL/RE/2017/04), Sabaragamuwa University of Sri Lanka, Belihuoya, Sri Lanka.

Author information

Authors and Affiliations

  1. Department of Physical Sciences and Technology, Faculty of Applied Sciences, Sabaragamuwa University of Sri Lanka, Belihuloya, Sri Lanka

    R. M. Kapila Tharanga Rathnayaka

  2. Department of Interdisciplinary Studies, Faculty of Engineering, University of Ruhuna, Galle, Sri Lanka

    D. M. K. N. Seneviratna

Authors
  1. R. M. Kapila Tharanga Rathnayaka
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  2. D. M. K. N. Seneviratna
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Corresponding author

Correspondence to R. M. Kapila Tharanga Rathnayaka .

Editor information

Editors and Affiliations

  1. Karunya University, Coimbatore, India

    Jude Hemanth

  2. University of Moratuwa, Moratuwa, Sri Lanka

    Thushari Silva

  3. University of Moratuwa, Moratuwa, Sri Lanka

    Asoka Karunananda

Codes and Mat Lab Program for Proposed Hybrid Methodology

Codes and Mat Lab Program for Proposed Hybrid Methodology

The new hybrid methodology is more appropriate to handle incomplete, noise and non-linear random time sequences with limited data samples. The proposed algorithm is as follows.

figure b

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Kapila Tharanga Rathnayaka, R.M., Seneviratna, D.M.K.N. (2019). A Novel Hybrid Back Propagation Neural Network Approach for Time Series Forecasting Under the Volatility. In: Hemanth, J., Silva, T., Karunananda, A. (eds) Artificial Intelligence. SLAAI-ICAI 2018. Communications in Computer and Information Science, vol 890. Springer, Singapore. https://doi.org/10.1007/978-981-13-9129-3_6

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  • DOI: https://doi.org/10.1007/978-981-13-9129-3_6

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-9128-6

  • Online ISBN: 978-981-13-9129-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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