Notice of Retraction: A Modified Model of Fuzzy Time Series for Forecasting Exchange Rates

2010 ◽  
Author(s):  
Kai Chi ◽  
Fang-Ping Fu ◽  
Wen-Gang Che
Keyword(s):  
Time Series ◽  
Exchange Rates ◽  
Fuzzy Time Series ◽  
Modified Model

Implementation of Fuzzy Time Series in Forecasting of the Non-Stationary Data

2016 ◽  
Vol 15 (02) ◽  
pp. 1650009 ◽  
Author(s):  
Riswan Efendi ◽  
Mustafa Mat Deris ◽  
Zuhaimy Ismail
Keyword(s):  
Time Series ◽  
Exchange Rates ◽  
Time Series Data ◽  
Data Type ◽  
Fuzzy Time Series ◽  
Stationary Time Series ◽  
Series Data ◽  
Stationary Time ◽  
Load Demand ◽  
Electricity Load

To forecast the non-stationary data is quite difficult when compared with the stationary data time series. Because their variances are not constant and not stable like the second data type. This paper presents the implementation of fuzzy time series (FTS) into the non-stationary time series data forecasting, such as, the electricity load demand, the exchange rates, the enrollment university and others. These data forecasts are derived by implementing of the weightage and linguistic out-sample methods. The result shows that the FTS can be applied in improving the accuracy and efficiency of these non-stationary data forecasting opportunities.

IMPROVED WEIGHT FUZZY TIME SERIES AS USED IN THE EXCHANGE RATES FORECASTING OF US DOLLAR TO RINGGIT MALAYSIA

2013 ◽  
Vol 12 (01) ◽  
pp. 1350005 ◽  
Author(s):  
RISWAN EFENDI ◽  
ZUHAIMY ISMAIL ◽  
MUSTAFA MAT DERIS
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Exchange Rates ◽  
Fuzzy Time Series ◽  
Business Environments ◽  
Forecasting Method ◽  
The Us ◽  
Us Dollar ◽  
Daily Forecast ◽  
Fuzzy Logical

Foreign exchange rate (forex) forecasting has been the subject of several rigorous investigations due to its importance in evaluating the benefits and risks of the international business environments. Many methods have been researched with the ultimate goal being to increase the reliability and efficiency of the forecasting method. However as the data are inherently dynamic and complex, the development of accurate forecasting method remains a challenging task if not a formidable one. This paper proposes a new weight of the fuzzy time series model for a daily forecast of the exchange rate market. Through this method, the weights are assigned to the fuzzy relationships based on a probability approach. This can be implemented to carry out the frequently recurring fuzzy logical relationship (FLR) in the fuzzy logical group (FLG). The US dollar to the Malaysian Ringgit (MYR) exchange rates are used as an example and the efficiency of the proposed method is compared with the methods proposed by Yu and Cheng et al. The result shows that the proposed method has enhanced the accuracy and efficiency of the daily exchange rate forecasting opportunities.

A distance-based fuzzy time series model for exchange rates forecasting

2009 ◽  
Vol 36 (4) ◽  
pp. 8107-8114 ◽  
Author(s):  
Yungho Leu ◽  
Chien-Pang Lee ◽  
Yie-Zu Jou
Keyword(s):  
Time Series ◽  
Exchange Rates ◽  
Time Series Model ◽  
Fuzzy Time Series

About One Approach to Defuzzifikation of Outputs of Fuzzy Time Series Model

2011 ◽  
Vol 3 (9) ◽  
pp. 562-566
Author(s):  
Ramin Rzayev ◽  
◽  
Musa Agamaliyev ◽  
Nijat Askerov
Keyword(s):  
Time Series ◽  
Time Series Model ◽  
Fuzzy Time Series

Peramalan Data IHSG Menggunakan Metode Backpropagation

2013 ◽  
Vol 5 (1) ◽  
pp. 26-30
Author(s):  
Seng Hansun
Keyword(s):  
Time Series ◽  
Soft Computing ◽  
Fuzzy Time Series

Jaringan saraf tiruan merupakan salah satu metode soft computing yang banyak digunakan dan diterapkan di berbagai disiplin ilmu, termasuk analisis data runtun waktu. Tujuan utama dari analisis data runtun waktu adalah untuk memprediksi data runtun waktu yang dapat digunakan secara luas dalam berbagai data runtun waktu real, termasuk data harga saham. Banyak peneliti yang telah berkontribusi dalam analisis data runtun waktu dengan menggunakan berbagai pendekatan berbeda. Chen dan Hsu, Jilani dkk., Stevenson dan Porter, dan Hansun telah menggunakan metode fuzzy time series untuk meramalkan data mendatang, sementara beberapa peneliti lainnya menggunakan metode hibrid, seperti yang dilakukan oleh Subanar dan Suhartono, Popoola dkk, Popoola, Hansun dan Subanar. Di dalam penelitian ini, penulis mencoba untuk menerapkan metode jaringan saraf tiruan backpropagation pada salah satu indikator perubahan harga saham, yakni IHSG (Indeks Harga Saham Gabungan). Penelitian dilanjutkan dengan menghitung tingkat akurasi dan kehandalan metode yang telah diterapkan pada data IHSG. Pendekatan ini diharapkan dapat menjadi salah satu cara alternatif dalam meramalkan data IHSG sebagai salah satu indikator perubahan harga saham di Indonesia. Kata kunci—jaringan saraf tiruan, backpropagation, analisis data runtun waktu, soft computing, IHSG

A Distributed Algorithm for Scalable Fuzzy Time Series

2019 ◽  
pp. 42-56 ◽  
Author(s):  
Petrônio Cândido de Lima e Silva ◽  
Patrícia de Oliveira e Lucas ◽  
Frederico Gadelha Guimarães
Keyword(s):  
Time Series ◽  
Distributed Algorithm ◽  
Fuzzy Time Series

Fuzzy time series for predicting phenological stages of apple trees

2021 ◽  
Author(s):  
Tiago Boechel ◽  
Lucas Micol Policarpo ◽  
Gabriel de Oliveira Ramos ◽  
Rodrigo da Rosa Righi
Keyword(s):  
Time Series ◽  
Fuzzy Time Series ◽  
Phenological Stages ◽  
Apple Trees

Evolving Fuzzy Time Series for Spatio-temporal Forecasting in Renewable Energy Systems

2021 ◽  
Author(s):  
Carlos A. Severiano ◽  
Petrônio de Cândido de Lima e Silva ◽  
Miri Weiss Cohen ◽  
Frederico Gadelha Guimarães
Keyword(s):  
Time Series ◽  
Renewable Energy ◽  
Energy Systems ◽  
Fuzzy Time Series ◽  
Renewable Energy Systems ◽  
Spatio Temporal
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