Multi-variable fuzzy forecasting based on fuzzy clustering and fuzzy rule interpolation techniques

Chen, Shyi‐Ming; Chang, Yu‐Chuan

doi:10.1016/j.ins.2010.08.026

Cited by 138 publications

(59 citation statements)

References 39 publications

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“…To make a comparison with the forecasting results of other researchers' work [29,30,41,42], we used the real Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) to show the forecasting procedure. We used the data from January to October of the given year as a training time series and the data from November to December of the same year as the testing dataset.…”

Section: New Forecasting Model Based On Two-factor Arma(1m) Fflrsmentioning

confidence: 99%

“…Some other soft computing techniques have been used to forecast in many studies [25][26][27]. In fact, fuzzy time series forecasting studies are frequently based on fuzzy autoregressive (AR) structures [28][29][30][31][32]. To further improve the performance of fuzzy AR models, an adaptive fuzzy inference system (ANFIS) [33] has been used in time series prediction [34][35][36][37].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: New Forecasting Model Based On Two-factor Arma(1m) Fflrsmentioning

confidence: 99%

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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“…Candidate solution (star) that crosses the event horizon of the black hole will be swallowed by the black hole. Then a new star following the swallowed one is generated and distributed randomly in the search space [28][29]. This generation is to keep the number of stars (candidate solutions) constant.…”

Section: Black Hole Algorithm (Bha)mentioning

confidence: 99%

Solving optimization problems using black hole algorithm

Farahmandian¹,

Hatamlou²

2015

JACST

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Various meta-heuristic optimization approaches have been recently created and applied in different areas. Many of these approaches are inspired by swarm behaviors in the nature. This paper studies the solving optimization problems using Black Hole Algorithm (BHA) which is a population-based algorithm. Since the performance of this algorithm was not tested in mathematical functions, we have studied this issue using some standard functions. The results of the BHA are compared with the results of GA and PSO algorithms which indicate that the performance of BHA is better than the other two mentioned algorithms.

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“…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]. These obtained logical relationships will be used as rules during the forecasting process.…”

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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Multi-variable fuzzy forecasting based on fuzzy clustering and fuzzy rule interpolation techniques

Cited by 138 publications

References 39 publications

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

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

Solving optimization problems using black hole algorithm

Forecasting Model Based on Neutrosophic Logical Relationship and Jaccard Similarity

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