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...

Full description

Bibliographic Details
Main Author: Dina Tri Utari
Format: Article
Language:Indonesian
Published: Universitas Islam Indonesia 2018-09-01
Series:Eksakta: Jurnal Ilmu-Ilmu MIPA
Subjects:
LSW
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