On Multivariate Fuzzy Time Series Analysis and Forecasting

2002 ◽  
pp. 363-372 ◽  
Author(s):  
Berlin Wu ◽  
Yu-Yun Hsu
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Fuzzy Time Series ◽  
Series Analysis

A NEW APPROACH OF BIVARIATE FUZZY TIME SERIES ANALYSIS TO THE FORECASTING OF A STOCK INDEX

2003 ◽  
Vol 11 (06) ◽  
pp. 671-690 ◽  
Author(s):  
YU-YUN HSU ◽  
SZE-MAN TSE ◽  
BERLIN WU
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Trading Volume ◽  
Arma Model ◽  
Future Trend ◽  
Fuzzy Time Series ◽  
Stock Index ◽  
New Approach ◽  
Series Analysis ◽  
Closing Price

In recent years, the innovation and improvement of forecasting techniques have caught more and more attention. Especially, in the fields of financial economics, management planning and control, forecasting provides indispensable information in decision-making process. If we merely use the time series with the closing price array to build a forecasting model, a question that arises is: Can the model exhibit the real case honestly? Since, the daily closing price of a stock index is uncertain and indistinct. A decision for biased future trend may result in the danger of huge lost. Moreover, there are many factors that influence daily closing price, such as trading volume and exchange rate, and so on. In this research, we propose a new approach for a bivariate fuzzy time series analysis and forecasting through fuzzy relation equations. An empirical study on closing price and trading volume of a bivariate fuzzy time series model for Taiwan Weighted Stock Index is constructed. The performance of linguistic forecasting and the comparison with the bivariate ARMA model are also illustrated.

Fuzzy Time Series Analysis

Fuzzy Control ◽  
2000 ◽  
pp. 331-345 ◽  
Author(s):  
Steffen F. Bocklisch ◽  
Michael Päßler
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Fuzzy Time Series ◽  
Series Analysis

scholarly journals Traffic Forecasting and Planning of WiMAX under Multiple Priority Using Fuzzy Time Series Analysis

2015 ◽  
Vol 03 (01) ◽  
pp. 68-74 ◽  
Author(s):  
Ismail Bin Abdullah ◽  
Daw Abdulsalam Ali Daw ◽  
Kamaruzzaman Bin Seman
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Fuzzy Time Series ◽  
Traffic Forecasting ◽  
Series Analysis

scholarly journals Peramalan Data IHSG Menggunakan Fuzzy Time Series

2013 ◽  
Vol 7 (1) ◽  
Author(s):  
Seng Hansun
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Time Series Analysis ◽  
Real Time ◽  
Soft Computing ◽  
Time Series Data ◽  
Fuzzy Time Series ◽  
Series Data ◽  
Time Data ◽  
Series Analysis

AbstrakFuzzy time series merupakan salah satu metode soft computing yang telah digunakan dan diterapkan dalam analisis data runtun waktu. Tujuan utama dari fuzzy time series adalah untuk memprediksi data runtun waktu yang dapat digunakan secara luas pada sembarang data real time, termasuk data pasar modal.Banyak peneliti yang telah berkontribusi dalam pengembangan analisis data runtun waktu menggunakan fuzzy time series, seperti Chen dan Hsu [1], Jilani dkk. [2], serta Stevenson dan Porter [3]. Dalam penelitian ini, dicoba untuk menerapkan metode fuzzy time series pada salah satu indikator pergerakan harga saham, yakni data IHSG (Indeks Harga Saham Gabungan).Kinerja metode yang diusulkan dievaluasi dengan menghitung tingkat akurasi dan tingkat kehandalan metode fuzzy time series yang diterapkan pada data IHSG. Melalui pendekatan ini, diharapkan metode fuzzy time series dapat menjadi alternatif untuk memprediksi data IHSG yang merupakan salah satu indikator pergerakan harga saham di Indonesia. Kata kunci – fuzzy time series, data runtun waktu, soft computing, IHSG AbstractFuzzy time series is one of the soft computing method that been used and implemented in time series analysis. The main goal of fuzzy time series is to predict time series data that can be used widely in any real time data, including stock market share.Many researchers have contributed in the development of fuzzy time series analysis, such as Chen and Hsu [1], Jilani [2], and Stevenson and Porter [3]. In this research, we will try to implement the fuzzy time series method in one of the stock market change indicator, i.e. the Jakarta composite index or also known as IHSG (Indeks Harga Saham Gabungan).The research is continued by calculating the accuracy and robustness of the method which has been implemented on IHSG data. By this approach, we hope it can be an alternative to predict the IHSG data which is an indicator of stock price changes in Indonesia. Keywords – fuzzy time series, time series data, soft computing, IHSG

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