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Abstract
Currency exchange rate of a country to the other countries is fluctuative. The movement of the exchange rate affects the country’s economy. The exchange rate can change any time according to the market mechanism, therefore currency exchange predictions is required to determine future economic policy. Based on the impact of exchange rate in economy fluctuations, an accurate model is needed to determine the exchange rate movements.
In this case, the model is Locally Stationary Wavelet (LSW). This model combines stocastic process class based on wavelet non decimated. LSW model can catch most of the information in time series data. Based on the application of LSW mtehod on the data of the rupiah against the US dollar for the period April 2016 - March 2017, it can be concluded that model provides forecasting results approaching actual data therefore it can be used for forecasting exchange rates. The value of the mean absolute percentage error (MAPE) is 0,1201293%.
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References
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- Fryzlewicz, P. 2005. Modelling and forecasting financial log-return as locally stationary wavelet processes. Journal of Applied Statistics, 32 (5), pp. 503-528.
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- Wijayanti, T., Abadi, A. M. dan Taufik, M. R. 2016. Application of Wavelet Fuzzy Model to Forecast the Exchange Rate IDR of USD. International Journal of Modeling and Optimization, Vol. 6, No. 1.
- Meirianti. 2016. Pengaruh Kemiskinan, Belanja Pemerintah Bidang Pendidikan, Kesehatan dan Ekonomi Terhadap Tingkat IPM di 38 Kabupaten/Kota Provinsi Jawa Timur, 2010-2014. Tugas Akhir. Yogyakarta : Universitas Gadjah Mada.
References
Fryzlewicz, P., Bellegem, S. V., dan Sachs, R. V. 2003. A wavelet-based model for forecasting non-stationary processes. IOP Publishing: Bristol.
Fryzlewicz, P. 2005. Modelling and forecasting financial log-return as locally stationary wavelet processes. Journal of Applied Statistics, 32 (5), pp. 503-528.
Hady, H. 2010. Manajemen Keuangan Internasional. Mitra Wacana Media: Jakarta.
Helmy, H. 2011. Aplikasi Peramalan Kurs Valuta Asing Rupiah per Dollar Amerika Serikat dengan Menggunakan Metode Box-Jenkins (ARIMA). TINGKAP Jurnal Ilmiah Ilmu-ilmu Sosial Budaya dan Ekonomi Vol VII No. 1. Universitas Negeri Padang.
Nason, G.P. 2006. Wavelet Methods in Statistics with R. Springer. Bristol: University Walk. Odgen, R.T. 1997. Essential Wavelets for Statistical Application and Data Analysis. Birkhauser: Boston.
Priestly, M. B. 1981. Spectral Analysis and Time Series. Academic Press: London.
Wijayanti, T., Abadi, A. M. dan Taufik, M. R. 2016. Application of Wavelet Fuzzy Model to Forecast the Exchange Rate IDR of USD. International Journal of Modeling and Optimization, Vol. 6, No. 1.
Meirianti. 2016. Pengaruh Kemiskinan, Belanja Pemerintah Bidang Pendidikan, Kesehatan dan Ekonomi Terhadap Tingkat IPM di 38 Kabupaten/Kota Provinsi Jawa Timur, 2010-2014. Tugas Akhir. Yogyakarta : Universitas Gadjah Mada.