PSO-based high order time invariant fuzzy time series method: Application to stock exchange data

2014 ◽  
Vol 38 ◽  
pp. 633-639 ◽  
Author(s):  
Erol Egrioglu
Keyword(s):  
Time Series ◽  
Stock Exchange ◽  
High Order ◽  
Fuzzy Time Series ◽  
Series Method ◽  
Time Series Method ◽  
High Order Time ◽  
Time Invariant ◽  
Exchange Data

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 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.

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.

scholarly journals A Novel Fuzzy Time Series Forecasting Model Based on Multiple Linear Regression and Time Series Clustering

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Yanpeng Zhang ◽  
Hua Qu ◽  
Weipeng Wang ◽  
Jihong Zhao
Keyword(s):  
Time Series ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Time Series Forecasting ◽  
High Order ◽  
Fuzzy Time Series ◽  
Forecasting Model ◽  
Time Series Clustering ◽  
Proposed Model ◽  
High Order Time

Time series forecasting models based on a linear relationship model show great performance. However, these models cannot handle the the data that are incomplete, imprecise, and ambiguous as the interval-based fuzzy time series models since the process of fuzzification is abandoned. This article proposes a novel fuzzy time series forecasting model based on multiple linear regression and time series clustering for forecasting market prices. The proposed model employs a preprocessing to transform the set of fuzzy high-order time series into a set of high-order time series, with synthetic minority oversampling technique. After that, a high-order time series clustering algorithm based on the multiple linear regression model is proposed to cluster dataset of fuzzy time series and to build the linear regression model for each cluster. Then, we make forecasting by calculating the weighted sum of linear regression models’ results. Also, a learning algorithm is proposed to train the whole model, which applies artificial neural network to learn the weights of linear models. The interval-based fuzzification ensures the capability to deal with the uncertainties, and linear model and artificial neural network enable the proposed model to learn both of linear and nonlinear characteristics. The experiment results show that the proposed model improves the average forecasting accuracy rate and is more suitable for dealing with these uncertainties.

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

scholarly journals High-Order Fuzzy Time Series Model Based on Generalized Fuzzy Logical Relationship

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Wangren Qiu ◽  
Xiaodong Liu ◽  
Hailin Li
Keyword(s):  
Time Series ◽  
Stock Exchange ◽  
Composite Index ◽  
High Order ◽  
Fuzzy Time Series ◽  
Time Series Models ◽  
Logical Relationship ◽  
Proposed Model ◽  
The University ◽  
Fuzzy Logical

In view of techniques for constructing high-order fuzzy time series models, there are three methods which are based on advanced algorithms, computational methods, and grouping the fuzzy logical relationships, respectively. The last kind model has been widely applied and researched for the reason that it is easy to be understood by the decision makers. To improve the fuzzy time series forecasting model, this paper presents a novel high-order fuzzy time series models denoted asGTS(M,N)on the basis of generalized fuzzy logical relationships. Firstly, the paper introduces some concepts of the generalized fuzzy logical relationship and an operation for combining the generalized relationships. Then, the proposed model is implemented in forecasting enrollments of the University of Alabama. As an example of in-depth research, the proposed approach is also applied to forecast the close price of Shanghai Stock Exchange Composite Index. Finally, the effects of the number of orders and hierarchies of fuzzy logical relationships on the forecasting results are discussed.

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