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Modelling the Behaviour of Currency Exchange Rates with Singular Spectrum Analysis and Artificial Neural Networks

Author

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  • Paulo Canas Rodrigues

    (Department of Statistics, Federal University of Bahia, Salvador 40170-110, Brazil)

  • Olushina Olawale Awe

    (Department of Statistics, Federal University of Bahia, Salvador 40170-110, Brazil
    Department of Mathematical Sciences, Anchor University Lagos, Lagos 100278, Nigeria)

  • Jonatha Sousa Pimentel

    (Department of Statistics, Federal University of Bahia, Salvador 40170-110, Brazil)

  • Rahim Mahmoudvand

    (Department of Statistics, Bu-Ali Sina University, Hamedan 6517833131, Iran)

Abstract

A proper understanding and analysis of suitable models involved in forecasting currency exchange rates dynamics is essential to provide reliable information about the economy. This paper deals with model fit and model forecasting of eight time series of historical data about currency exchange rate considering the United States dollar as reference. The time series techniques: classical autoregressive integrated moving average model, the non-parametric univariate and multivariate singular spectrum analysis (SSA), artificial neural network (ANN) algorithms, and a recent prominent hybrid method that combines SSA and ANN, are considered and their performance compared in terms of model fit and model forecasting. Moreover, specific methodological and computational adaptations were conducted to allow for these analyses and comparisons.

Suggested Citation

  • Paulo Canas Rodrigues & Olushina Olawale Awe & Jonatha Sousa Pimentel & Rahim Mahmoudvand, 2020. "Modelling the Behaviour of Currency Exchange Rates with Singular Spectrum Analysis and Artificial Neural Networks," Stats, MDPI, vol. 3(2), pages 1-21, June.
    • Handle: RePEc:gam:jstats:v:3:y:2020:i:2:p:12-157:d:366020
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    2. Juan D. Borrero & Jesús Mariscal & Alfonso Vargas-Sánchez, 2022. "A New Predictive Algorithm for Time Series Forecasting Based on Machine Learning Techniques: Evidence for Decision Making in Agriculture and Tourism Sectors," Stats, MDPI, vol. 5(4), pages 1-14, November.
    3. Awe, Olushina Olawale & Dias, Ronaldo, 2022. "Comparative Analysis of ARIMA and Artificial Neural Network Techniques for Forecasting Non-Stationary Agricultural Output Time Series," AGRIS on-line Papers in Economics and Informatics, Czech University of Life Sciences Prague, Faculty of Economics and Management, vol. 14(4), December.

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