Forecasting in high order fuzzy times series by using neural networks to define fuzzy relations

Aladağ, Çağdaş Hakan; Başaran, Murat Alper; Eğrioğlu, Erol; Yolcu, Ufuk; Uslu, Vedide Rezan

doi:10.1016/j.eswa.2008.04.001

Cited by 151 publications

(74 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…To compare the performance of the C-R-FTSM in case of outliers, we use some current models in the literature: SC93 [82], C96 [23], H01 1 [57] (average based), H01 2 [57] (distribution based), HY06 [59], A09 [2], Y13 [98], and E15 [13]. …”

Section: Performance Measurementioning

confidence: 99%

A combined robust fuzzy time series method for prediction of time series

Yolcu

Lam

2017

Neurocomputing

View full text Add to dashboard Cite

In case of outlier(s) it is inevitable that the performance of the fuzzy time series prediction methods is influenced adversely. Therefore, current prediction methods will not be able to provide satisfactory accuracy rates for defuzzified outputs (predictions) when the data has outlier(s). In this study, not only to be able to sort out this problem but also to be able to improve the forecasting accuracy, we propose a combined robust approach for fuzzy time series by assessing how the prediction performance of the methods will be affected from the

show abstract

Section: Performance Measurementioning

confidence: 99%

A combined robust fuzzy time series method for prediction of time series

Yolcu

Lam

2017

Neurocomputing

View full text Add to dashboard Cite

show abstract

“…To state appropriate fuzzy logical relationships, Yu [25] proposed a weight assignation model, based on the recurrent fuzzy relationships, for each individual relationship. Aladag et al [26] considered artificial neural networks to be a basic high-order method for the establishment of logical relationships. Fuzzy auto regressive (AR) models and fuzzy auto regressive and moving average (ARMA) models are also widely used to reflect the recurrence and weights of different fuzzy logical relationships [9,10,[27][28][29][30][31][32][33][34][35].…”

Section: Introductionmentioning

confidence: 99%

Forecasting Model Based on Neutrosophic Logical Relationship and Jaccard Similarity

Guan

Zhao

2017

Symmetry

View full text Add to dashboard Cite

Abstract:The daily fluctuation trends of a stock market are illustrated by three statuses: up, equal, and down. These can be represented by a neutrosophic set which consists of three functions-truth-membership, indeterminacy-membership, and falsity-membership. In this paper, we propose a novel forecasting model based on neutrosophic set theory and the fuzzy logical relationships between the status of historical and current values. Firstly, the original time series of the stock market is converted to a fluctuation time series by comparing each piece of data with that of the previous day. The fluctuation time series is then fuzzified into a fuzzy-fluctuation time series in terms of the pre-defined up, equal, and down intervals. Next, the fuzzy logical relationships can be expressed by two neutrosophic sets according to the probabilities of different statuses for each current value and a certain range of corresponding histories. Finally, based on the neutrosophic logical relationships and the status of history, a Jaccard similarity measure is employed to find the most proper logical rule to forecast its future. The authentic Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) time series datasets are used as an example to illustrate the forecasting procedure and performance comparisons. The experimental results show that the proposed method can successfully forecast the stock market and other similar kinds of time series. We also apply the proposed method to forecast the Shanghai Stock Exchange Composite Index (SHSECI) to verify its effectiveness and universality.

show abstract

“…This technique has been used in subsequent studies. Moreover, the artificial neural network was also used in the determination of fuzzy relations (Huarng and Yu 2006b;Aladag et al 2009;Egrioglu et al 2009a,b,c;Huarng 2008, 2010;Alpaslan et al 2012). In addition in another study, while fuzzy logic relationship tables were used in the identification of fuzzy relations, estimating based on the next state for training set and master voting scheme for test set were used (Yolcu et al 2014).…”

Section: Introductionmentioning

confidence: 99%

“…For many data sets encountered in real life a high-order fuzzy time series forecasting model would be more appropriate to be analyzed, while a first-order fuzzy time series forecasting model can be enough to fit to some fuzzy time series data. In the some papers, the first-order fuzzy time series forecasting model was used Chissom 1993a,b, 1994;Chen 1996;Huarng 2008, 2010 The high-order fuzzy time series forecasting model was employed to analyze the data sets in a number of studies (Chen 2002;Aladag et al 2009;Egrioglu et al 2009aEgrioglu et al ,b,c, 2010.…”

Section: Introductionmentioning

confidence: 99%

The Forecasting Of Labour Force Participation And The Unemployment Rate In Poland And Turkey Using Fuzzy Time Series Methods

Yolcu

Baş

2016

CER

Self Cite

View full text Add to dashboard Cite

Fuzzy time series methods based on the fuzzy set theory proposed by Zadeh (1965) was first introduced by Song and Chissom (1993). Since fuzzy time series methods do not have the assumptions that traditional time series do and have effective forecasting performance, the interest on fuzzy time series approaches is increasing rapidly. Fuzzy time series methods have been used in almost all areas, such as environmental science, economy and finance. The concepts of labour force participation and unemployment have great importance in terms of both the economy and sociology of countries. For this reason there are many studies on their forecasting. In this study, we aim to forecast the labour force participation and unemployment rate in Poland and Turkey using different fuzzy time series methods.

show abstract

Forecasting in high order fuzzy times series by using neural networks to define fuzzy relations

Cited by 151 publications

References 10 publications

A combined robust fuzzy time series method for prediction of time series

A combined robust fuzzy time series method for prediction of time series

Forecasting Model Based on Neutrosophic Logical Relationship and Jaccard Similarity

The Forecasting Of Labour Force Participation And The Unemployment Rate In Poland And Turkey Using Fuzzy Time Series Methods

Contact Info

Product

Resources

About