A new explainable robust high-order intuitionistic fuzzy time-series method

2021 ◽  
Author(s):  
Cem Kocak ◽  
Erol Egrioglu ◽  
Eren Bas
Keyword(s):  
Time Series ◽  
High Order ◽  
Fuzzy Time Series ◽  
Series Method ◽  
Intuitionistic Fuzzy ◽  
Time Series Method

A New Explainable Robust High Order Intuitionistic Fuzzy Time Series Method

2021 ◽  
Author(s):  
Cem Kocak ◽  
Erol Eğrioğlu ◽  
Eren Bas
Keyword(s):  
Time Series ◽  
Robust Regression ◽  
Time Series Forecasting ◽  
High Order ◽  
Fuzzy Time Series ◽  
Time Series Models ◽  
Series Method ◽  
Fuzzy Relations ◽  
Intuitionistic Fuzzy ◽  
Time Series Method

Abstract Fuzzy time series forecasting methods based on type-1 fuzzy sets continue to have largely proposed in the literature. These methods use only membership values in determining fuzzy relations. However, Intuitionistic fuzzy time series models basically use both membership values and non-membership values. So, it can be considered that the using of intuitionistic fuzzy time forecasting models will be able to increase the forecasting performance in the fuzzy time series analyses because of the fact that more information is used. Therefore, Intuitionistic fuzzy time series models have started to use for solving the real-life series in the fuzzy time series literature since 2013. In this study, a new explainable robust high order intuitionistic fuzzy time series forecasting method are proposed based on new defined model. In the proposed method, the algorithm of intuitionistic fuzzy c-means is used for fuzzification of observations and a robust regression method is used to determine fuzzy relations. Because the robust regression is employed to define fuzzy relation, all inputs of the method can be explainable and they can be statistically tested and commented. Applications of this study have been made by using energy data of Primary Energy Consumption (PEC) between 1965 and 2016 for 23 countries in the region of Europe&Eurasia. Forecasting performances obtained from these applications by using the proposed method have been compared with performances of some other time series method in the literature and the results have been discussed.

A Novel Intuitionistic Fuzzy Time Series Method Based on Bootstrapped Combined Pi-Sigma Artificial Neural Network

2021 ◽  
Vol 01 ◽  
Author(s):  
Eren Bas ◽  
Erol Egrioglu ◽  
Emine Kölemen
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Stock Exchange ◽  
Fuzzy Time Series ◽  
Time Series Models ◽  
Intuitionistic Fuzzy ◽  
Forecasting Methods ◽  
Time Series Method ◽  
Artificial Neural

Background: Intuitionistic fuzzy time series forecasting methods have been started to solve the forecasting problems in the literature. Intuitionistic fuzzy time series methods use both membership and non-membership values as auxiliary variables in their models. Because intuitionistic fuzzy sets take into consideration the hesitation margin and so the intuitionistic fuzzy time series models use more information than fuzzy time series models. The background of this study is about intuitionistic fuzzy time series forecasting methods. Objective: The study aims to propose a novel intuitionistic fuzzy time series method. It is expected that the proposed method will produce better forecasts than some selected benchmarks. Method: The proposed method uses bootstrapped combined Pi-Sigma artificial neural network and intuitionistic fuzzy c-means. The combined Pi-Sigma artificial neural network is proposed to model the intuitionistic fuzzy relations. Results and Conclusion: The proposed method is applied to different sets of SP&500 stock exchange time series. The proposed method can provide more accurate forecasts than established benchmarks for the SP&500 stock exchange time series. The most important contribution of the proposed method is that it creates statistical inference: probabilistic forecasting, confidence intervals and the empirical distribution of the forecasts. Moreover, the proposed method is better than the selected benchmarks for the SP&500 data set.

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

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