Prediction by using spectral analysis and artificial neural networks methodologies: Comparison Study
DOI:
https://doi.org/10.29304/jqcm.2017.9.2.326Keywords:
Time series analysis, spectral analysis, periodogram, Autoregressive Moving Average models, Artificial neural network, Backpropagation algorithm.Abstract
The variations in exchange rate, especially the sudden unexpected increases and decreases, have significant impact on the national economy of any country. Iraq is no exception; therefore, the accurate forecasting of exchange rate of Iraqi dinar to US dollar plays an important role in the planning and decision-making processes as well as the maintenance of a stable economy in Iraq. This research aims to compare spectral analysis methodology to artificial neural networks in terms of forecasting the exchange rate of Iraqi dinar to US dollar based on data provided by the Iraqi Central Bank for the period 30/01/2004 and 30/12/2014.
Based on the Mean Square Error (MSE), the Mean Absolute Error (MAE), and the Mean Absolute Percentage Error (MAPE) as criteria to compare the two methodologies, it was concluded that is artificial neural networks better than spectral analysis approach in forecasting.
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