Comparison between MEMD-LSSVM AND MEMD-ARIMA in forecasting exchange rate

Nur Izzati Abdul Rashid, Ani Shabri, Ruhaidah Samsudin

Research output: Contribution to journalArticle

Abstract

Due to the non-stationary and non-linearity behaviors of exchange rate data, an appropriate forecasting model that can capture these behaviors is crucial. This paper comparing the performance of modified empirical mode decomposition (EMD) and autoregressive integrated moving average (ARIMA) named as MEMD-ARIMA and modified empirical mode decomposition (EMD) and least squares support vector machine (LSSVM) named as MEMD-LSSVM in forecasting daily USD/TWD exchange rate. EMD technique is firstly used to decompose the exchange rate data that resulting in few intrinsic mode function (IMF) and one residual. In order to improve the result of the EMD so that more effective input can be provided to the forecasting models which are LSSVM and ARIMA, they are clustered into several groups via permutation distribution clustering (PDC). The successfulness of LSSVM in forecasting is depending on the input number selection. The problem is the input number selection is not based on any theories or techniques. Therefore, partial autocorrelation function (PACF) is used in this paper in determining the best number of input for LSSVM. This paper finds that the implementations of PDC has improved the performance of EMD-LSSVM and EMD-ARIMA and also suggest the PDC is suitable either for linear or non-linear model.

Original languageEnglish
Pages (from-to)328-339
Number of pages12
JournalJournal of Theoretical and Applied Information Technology
Volume95
Issue number2
StatePublished - 31 Jan 2017

Fingerprint

Least squares support vector machine
Support vector machines
Decompose
Decomposition
Moving average
Forecasting
Exchange rate
Clustering
Permutation
Model
Ion exchange
Partial autocorrelation
Intrinsic mode function
Decomposition techniques
Permutation group
Autocorrelation function
Nonlinear model
Nonlinearity
Autocorrelation

Keywords

  • Empirical Mode Decomposition (EMD)
  • Exchange rate
  • Forecasting
  • Least Squares Support Vector Machine (LSSVM)
  • Permutation Distribution Clustering (PDC)

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Comparison between MEMD-LSSVM AND MEMD-ARIMA in forecasting exchange rate. / Rashid, Nur Izzati Abdul; Shabri, Ani; Samsudin, Ruhaidah.

In: Journal of Theoretical and Applied Information Technology, Vol. 95, No. 2, 31.01.2017, p. 328-339.

Research output: Contribution to journalArticle

Rashid, Nur Izzati Abdul; Shabri, Ani; Samsudin, Ruhaidah / Comparison between MEMD-LSSVM AND MEMD-ARIMA in forecasting exchange rate.

In: Journal of Theoretical and Applied Information Technology, Vol. 95, No. 2, 31.01.2017, p. 328-339.

Research output: Contribution to journalArticle

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