Performance of Exchange Rate Forecast Using Distance-Based Fuzzy Time Series
نویسنده
چکیده
Fuzzy time series model has been employed by many researchers in forecasting activities such as students’ enrolment, temperature fluctuations and stock prices. The existing fuzzy time series models require exact match of the fuzzy logic relationships to calculate the forecasted value. However, in real life applications, the exact match of fuzzy logic relationships is not possible. Thus, an improved fuzzy time series model termed as distance-based fuzzy time series model was proposed to remedy this shortcoming and successfully tested to the case of exchange rate data of New Taiwan Dollar (NTD) against United States Dollar (USD). The model was reportedly outperformed the artificial neural network and random walk models for the NTD against USD exchange rate. However, the performance of exchange rate using the distance-based fuzzy times series model for other currencies is still not fully explored. This paper forecasts the exchange rate of Malaysian Ringgit (MYR) against USD and tests the performance of the exchange rate using a distance-based fuzzy time series model. Data of the exchange rate USD against MYR from 11 August 2009 to 15 September 2009 were tested to the forecasting model. A sample of performance comparison between data sets of MYR against USD and NTD against USD was conducted. Under the same forecasting model, it is found that the forecasting errors for MYR against USD were smaller than NTD against USD exchange rate. The experiment results show that the forecasted exchange rate of MYR against USD has performed better under the distance-based fuzzy time series model. KeywordFuzzy time series, Exchange rate, Euclidean distance, Fuzzy rules, Forecasting error
منابع مشابه
Comparison of Neural Network Models, Vector Auto Regression (VAR), Bayesian Vector-Autoregressive (BVAR), Generalized Auto Regressive Conditional Heteroskedasticity (GARCH) Process and Time Series in Forecasting Inflation in Iran
This paper has two aims. The first is forecasting inflation in Iran using Macroeconomic variables data in Iran (Inflation rate, liquidity, GDP, prices of imported goods and exchange rates) , and the second is comparing the performance of forecasting vector auto regression (VAR), Bayesian Vector-Autoregressive (BVAR), GARCH, time series and neural network models by which Iran's inflation is for...
متن کاملA hybrid computational intelligence model for foreign exchange rate forecasting
Computational intelligence approaches have gradually established themselves as a popular tool for forecasting the complicated financial markets. Forecasting accuracy is one of the most important features of forecasting models; hence, never has research directed at improving upon the effectiveness of time series models stopped. Nowadays, despite the numerous time series forecasting models propos...
متن کاملComputational intelligence approaches and linear models in case studies of forecasting exchange rates
Artificial neural networks and fuzzy systems, have gradually established themselves as a popular tool in approximating complicated nonlinear systems and time series forecasting. This paper investigates the hypothesis that the nonlinear mathematical models of multilayer perceptron and radial basis function neural networks and the Takagi–Sugeno (TS) fuzzy system are able to provide a more accurat...
متن کاملInterpolating time series based on fuzzy cluster analysis problem
This study proposes the model for interpolating time series to use them to forecast effectively for future. This model is established based on the improved fuzzy clustering analysis problem, which is implemented by the Matlab procedure. The proposed model is illustrated by a data set and tested for many other datasets, especially for 3003 series in M3-Competition data. Comparing to the exist...
متن کاملA Hybrid Time Series Clustering Method Based on Fuzzy C-Means Algorithm: An Agreement Based Clustering Approach
In recent years, the advancement of information gathering technologies such as GPS and GSM networks have led to huge complex datasets such as time series and trajectories. As a result it is essential to use appropriate methods to analyze the produced large raw datasets. Extracting useful information from large data sets has always been one of the most important challenges in different sciences,...
متن کامل