A new linguistic out-sample approach of fuzzy time series for daily forecasting of Malaysian electricity load demand

Efendi, Riswan; Ismail, Zuhaimy; Deris, Mustafa Mat

doi:10.1016/j.asoc.2014.11.043

Cited by 87 publications

(46 citation statements)

References 52 publications

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“…Although several different forecasting methods are used for prediction of electricity demand, none of them is superior in all cases. Some of these techniques used to forecast electricity demand of countries are the time series model (Saab et al, 2001;Sa'ad, 2009;Dilaver and Hunt, 2011;Boran, 2014;Efendi et al, 2014), artificial neural networks (ANNs) model (Hamzacebi and Kutay, 2004;Hamzacebi, 2007;Azadeh et al, 2008;Cunkas and Altun, 2010;Panklib et al, 2015) , regression and econometric model (Mohamed and Bodger, 2005;Al-Shobaki and Mohsen, 2008;Meng and Niu, 2011;Bildirici et al, 2012;Bianco et al, 2013), neuro-fuzyy model (Demirel et al, 2010;Chang et al, 2011), heuristic optimization method (El-Telbany and ElKarmi, 2008;Cunkas and Taskiran, 2011;Zhu et al, 2011), and support vector regression model (SVR) (De Felice et al, 2015;Jain et al, 2014;Kaytez et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Primary energy sources planning based on demand forecasting: The case of Turkey

Hamzaçebi

2016

J. energy South. Afr.

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Section: Introductionmentioning

confidence: 99%

Primary energy sources planning based on demand forecasting: The case of Turkey

Hamzaçebi

2016

J. energy South. Afr.

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“…The biggest differences between these fuzzy time series models are the detailed partitioning method and the trend rules. Efendi et al [17] used a fuzzy time series model to forecast daily electricity load demand. Sadaei et al [18] proposed a short-term load forecasting model based on the seasonality memory process and fuzzy time series model.…”

Section: Introductionmentioning

confidence: 99%

A Two-Factor Autoregressive Moving Average Model Based on Fuzzy Fluctuation Logical Relationships

Guan

Zhao

2017

Symmetry

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Many of the existing autoregressive moving average (ARMA) forecast models are based on one main factor. In this paper, we proposed a new two-factor first-order ARMA forecast model based on fuzzy fluctuation logical relationships of both a main factor and a secondary factor of a historical training time series. Firstly, we generated a fluctuation time series (FTS) for two factors by calculating the difference of each data point with its previous day, then finding the absolute means of the two FTSs. We then constructed a fuzzy fluctuation time series (FFTS) according to the defined linguistic sets. The next step was establishing fuzzy fluctuation logical relation groups (FFLRGs) for a two-factor first-order autoregressive (AR (1)) model and forecasting the training data with the AR(1) model. Then we built FFLRGs for a two-factor first-order autoregressive moving average (ARMA(1,m)) model. Lastly, we forecasted test data with the ARMA(1,m) model. To illustrate the performance of our model, we used real Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) and Dow Jones datasets as a secondary factor to forecast TAIEX. The experiment results indicate that the proposed two-factor fluctuation ARMA method outperformed the one-factor method based on real historic data. The secondary factor may have some effects on the main factor and thereby impact the forecasting results. Using fuzzified fluctuations rather than fuzzified real data could avoid the influence of extreme values in historic data, which performs negatively while forecasting. To verify the accuracy and effectiveness of the model, we also employed our method to forecast the Shanghai Stock Exchange Composite Index (SHSECI) from 2001 to 2015 and the international gold price from 2000 to 2010.

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“…Therefore, Song and Chissom proposed the fuzzy time series (FTS) forecasting model [6][7][8] to predict the future of such nonlinear and complicated problems. In a financial context, FTS approaches have been widely applied to stock index forecasting [9][10][11][12][13]. In order to improve the accuracy of forecasts for stock market indices, some researchers combine fuzzy and non-fuzzy time series with heuristic optimization methods in their forecasting strategies [14].…”

Section: Introductionmentioning

confidence: 99%

Forecasting Model Based on Neutrosophic Logical Relationship and Jaccard Similarity

Guan

Zhao

2017

Symmetry

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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.

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A new linguistic out-sample approach of fuzzy time series for daily forecasting of Malaysian electricity load demand

Cited by 87 publications

References 52 publications

Primary energy sources planning based on demand forecasting: The case of Turkey

Primary energy sources planning based on demand forecasting: The case of Turkey

A Two-Factor Autoregressive Moving Average Model Based on Fuzzy Fluctuation Logical Relationships

Forecasting Model Based on Neutrosophic Logical Relationship and Jaccard Similarity

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