Forecasting The Exchange Rate (IDR) of US Dollar (USD) Using Locally Stationary Wavelet
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 poli...
Main Author: | |
---|---|
Format: | Article |
Language: | Indonesian |
Published: |
Universitas Islam Indonesia
2018-09-01
|
Series: | Eksakta: Jurnal Ilmu-Ilmu MIPA |
Subjects: | |
Online Access: | https://journal.uii.ac.id/Eksakta/article/view/10327 |
id |
doaj-99806ea9bf9d41c597b3be8bd3ad567b |
---|---|
record_format |
Article |
spelling |
doaj-99806ea9bf9d41c597b3be8bd3ad567b2020-11-24T22:14:36ZindUniversitas Islam IndonesiaEksakta: Jurnal Ilmu-Ilmu MIPA1411-10472503-23642018-09-0118214515410.20885/eksakta.vol18.iss2.art68267Forecasting The Exchange Rate (IDR) of US Dollar (USD) Using Locally Stationary WaveletDina Tri Utari0Program Studi Statistika, FMIPA, Universitas Islam Indonesia, YogyakartaCurrency 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%.https://journal.uii.ac.id/Eksakta/article/view/10327exchange ratediscrete wavelet transformsLSWforecasting |
collection |
DOAJ |
language |
Indonesian |
format |
Article |
sources |
DOAJ |
author |
Dina Tri Utari |
spellingShingle |
Dina Tri Utari Forecasting The Exchange Rate (IDR) of US Dollar (USD) Using Locally Stationary Wavelet Eksakta: Jurnal Ilmu-Ilmu MIPA exchange rate discrete wavelet transforms LSW forecasting |
author_facet |
Dina Tri Utari |
author_sort |
Dina Tri Utari |
title |
Forecasting The Exchange Rate (IDR) of US Dollar (USD) Using Locally Stationary Wavelet |
title_short |
Forecasting The Exchange Rate (IDR) of US Dollar (USD) Using Locally Stationary Wavelet |
title_full |
Forecasting The Exchange Rate (IDR) of US Dollar (USD) Using Locally Stationary Wavelet |
title_fullStr |
Forecasting The Exchange Rate (IDR) of US Dollar (USD) Using Locally Stationary Wavelet |
title_full_unstemmed |
Forecasting The Exchange Rate (IDR) of US Dollar (USD) Using Locally Stationary Wavelet |
title_sort |
forecasting the exchange rate (idr) of us dollar (usd) using locally stationary wavelet |
publisher |
Universitas Islam Indonesia |
series |
Eksakta: Jurnal Ilmu-Ilmu MIPA |
issn |
1411-1047 2503-2364 |
publishDate |
2018-09-01 |
description |
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%. |
topic |
exchange rate discrete wavelet transforms LSW forecasting |
url |
https://journal.uii.ac.id/Eksakta/article/view/10327 |
work_keys_str_mv |
AT dinatriutari forecastingtheexchangerateidrofusdollarusdusinglocallystationarywavelet |
_version_ |
1725798062555660288 |