A Revised Fuzzy Time Series Method

2010 ◽  
Vol 171-172 ◽  
pp. 140-143
Author(s):  
Yan Hua Yu ◽  
Li Xia Song
Keyword(s):  
Time Series ◽  
Seasonal Variation ◽  
Fuzzy Regression ◽  
Fuzzy Time Series ◽  
Average Error ◽  
Series Method ◽  
First Order ◽  
Time Series Method ◽  
Time Invariant ◽  
Regression Theory

In this paper,we presents two methods to forecast secular trend and seasonal variation time series problems respectively. The revised fuzzy time series method uses Song and Chrisom’s first-order time-invariant model to predict such linguistic historical data problems. This method obtains a better average error than the error in Song and Chrisom’s method. The method using fuzzy regression theory solves the shortcoming that fuzzy time series method could not work in dealing with seasonal variation time series problems.

scholarly journals PERAMALAN JUMLAH WISATAWAN AUSTRALIA YANG BERKUNJUNG KE BALI MENGGUNAKAN MULTIVARIAT FUZZY TIME SERIES

2015 ◽  
Vol 4 (3) ◽  
pp. 90
Author(s):  
I MADE CANDRA SATRIA ◽  
I KOMANG GDE SUKARSA ◽  
KETUT JAYANEGARA
Keyword(s):  
Time Series ◽  
Error Rate ◽  
Fuzzy Time Series ◽  
Average Error ◽  
Series Method ◽  
Change Rate ◽  
Average Error Rate ◽  
Time Series Method ◽  
Is Development ◽  
Three Factor

The aim of this paper is to forecast the numbers of Australian tourist to Bali using multivariate fuzzy time series method (MFTS). MFTS method is development from fuzzy time series (FTS). The defferent betwen FTS and MFTS method is showed by factor in used. In FTS method using one factor, but in MFTS method using more than one factor. In this peper there was three factor used in this research, it was number of Australian tourist, Indonesian Inflation, and change rate of AUD to IDR. At the beginning, the speed of each factor was calculated. For each factor given weight, 0,999 for numbers of Australian tourist, -0,90 for Indonesian inflation, and 0,21 for change rate of AUD to IDR. The result showed that Australian tourist at July 2014 would visit Bali as much 91.056 tourist, with average error rate 6.87%.

scholarly journals IMPLEMENTASI METODE FUZZY TIME SERIES DENGAN MODEL ALGORITMA CHEN UNTUK MEMPREDIKSI HARGA EMAS

2020 ◽  
Vol 13 (1) ◽  
pp. 71-78
Author(s):  
Darsono Nababan ◽  
Eric Alexander
Keyword(s):  
Time Series ◽  
Interest Rates ◽  
Gold Price ◽  
Fuzzy Time Series ◽  
Actual Data ◽  
Series Method ◽  
Market Uncertainty ◽  
Low Interest Rates ◽  
Calculation Results ◽  
Time Series Method

Gold is one of the people's preferred forms of investment and is considered the safest (save -heaven). Gold risk which is considered small is the main attraction because in general Indonesian people are not yet familiar with capital market investments such as stocks and mutual funds. But the price of gold is very volatile as for the factors that affect the fluctuations of gold are consumption demand, volatility and market uncertainty, protection of low-interest rates, and the US dollar. Predicting the movement of the gold price and knowing where the direction of the exchange rate moves and determining the price of gold up or down cannot be done accurately and consistently. For this reason, in reducing the risk of loss, an application is needed to predict gold prices using the Fuzzy Time Series Chen algorithm using MATLAB software. In this study to obtain prediction results and comparison charts using actual data and prediction data for the 2015-2017 gold price. From the calculation results obtained by the prediction results with the Fuzzy Time Series method with the Chen algorithm where the average difference between the actual data and prediction data is not more than Rp. 2,850, - where predictions using the Fuzzy Time Series method Chen's algorithm is sufficient to use 1 data to predict the second data which makes this method accurate in predicting the price of gold.

A new fuzzy time series method based on an ARMA-type recurrent Pi-Sigma artificial neural network

2019 ◽  
Vol 24 (11) ◽  
pp. 8243-8252 ◽  
Author(s):  
Cem Kocak ◽  
Ali Zafer Dalar ◽  
Ozge Cagcag Yolcu ◽  
Eren Bas ◽  
Erol Egrioglu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Fuzzy Time Series ◽  
Series Method ◽  
Time Series Method ◽  
Artificial Neural

PERBANDINGAN FUZZY TIME SERIES DENGAN METODE CHEN DAN METODE S. R. SINGH (Studi Kasus : Nilai Impor di Jawa Tengah Periode Januari 2014 – Desember 2019)

2020 ◽  
Vol 9 (3) ◽  
pp. 306-315
Author(s):  
Febyani Rachim ◽  
Tarno Tarno ◽  
Sugito Sugito
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Fuzzy Time Series ◽  
Series Data ◽  
Percentage Error ◽  
Series Method ◽  
Fuzzy Variables ◽  
Central Java ◽  
Time Series Method ◽  
Fuzzy Logical

Import is one of the efforts of an area to meet the needs of its population in order to stabilize prices and maintain stock availability. The value of imports in Central Java throughout 2016 amounted to 8811.05 Million US Dollars. The value of imports in Central Java is the top 10 in all provinces in Indonesia with a percentage of 6.50%. Import data in Central Java is included in the time series data category. To maintain the stability of imports in Central Java, it is deemed necessary to make a plan based on a statistical model. One of the time series models that can be applied is the fuzzy time series model with the Chen method approach and the S. R. Singh method because the method is suitable for cyclical patterned data with monthly time periods such as Import data in Central Java. Important concepts in the preparation of the model are fuzzy sets, membership functions, set basic operators, fuzzy variables, universe sets and domains. The fuzzy time series modeling procedure is carried out through several stages, namely the determination of universe discourse which is divided into several intervals, then defines the fuzzy set so that it can be performed fuzzification. After that the fuzzy logical relations and fuzzy logical group relations are determined. The accuracy calculation in both methods uses symmetric Mean Absolute Percentage Error (sMAPE). In this study the sMAPE value obtained in the Fuzzy Time Series Chen method of 10.95% means that it shows good forecasting ability. While the sMAPE value on the Fuzzy Time Series method of S. R. Singh method by 5.50% shows very good forecasting ability. It can be concluded that the sMAPE value in the S. R. Singh fuzzy time series method is better than the Chen method.Keywords: Import value, fuzzy time series , Chen, S. R. Singh, sMAPE

scholarly journals First-Order ARMA Type Fuzzy Time Series Method Based on Fuzzy Logic Relation Tables

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Cem Kocak
Keyword(s):  
Time Series ◽  
Fuzzy Logic ◽  
Stock Exchange ◽  
Life Time ◽  
Fuzzy Time Series ◽  
Moving Averages ◽  
First Order ◽  
Forecasting Method ◽  
Forecasting Performance ◽  
Time Series Method

Fuzzy time series approaches have an important deficiency according to classical time series approaches. This deficiency comes from the fact that all of the fuzzy time series models developed in the literature use autoregressive (AR) variables, without any studies that also make use of moving averages (MAs) variables with the exception of only one study (Egrioglu et al. (2013)). In order to eliminate this deficiency, it is necessary to have many of daily life time series be expressed with Autoregressive Moving Averages (ARMAs) models that are based not only on the lagged values of the time series (AR variables) but also on the lagged values of the error series (MA variables). To that end, a new first-order fuzzy ARMA(1,1) time series forecasting method solution algorithm based on fuzzy logic group relation tables has been developed. The new method proposed has been compared against some methods in the literature by applying them on Istanbul Stock Exchange national 100 index (IMKB) and Gold Prices time series in regards to forecasting performance.

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