OWA-based ANFIS model for TAIEX forecasting

Cheng, Ching‐Hsue; Wei, Liang-Ying; Liu, Jingwei; Chen, Tai‐Liang

doi:10.1016/j.econmod.2012.09.047

Cited by 69 publications

(45 citation statements)

References 30 publications

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“…In order to compare the forecasting results with other researchers' work [9,17,23,25,[44][45][46][47][48], the authentic TAIEX (Taiwan Stock Exchange Capitalization Weighted Stock Index) is employed to illustrate the forecasting process. The data from January 1999 to October 1999 are used as the training time series and the data from November 1999 to December 1999 are used as the testing dataset.…”

Section: A Novel Forecasting Model Based On Neutrosophic Logical Relamentioning

confidence: 99%

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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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Section: A Novel Forecasting Model Based On Neutrosophic Logical Relamentioning

confidence: 99%

“…Many studies use TAIEX1999 as an example to illustrate their proposed forecasting methods [9,17,25,34,[44][45][46][47][48]. In order to compare the accuracy with their models, we also use TAIEX1999 to Step 1.…”

Section: Forecasting Taiwan Stock Exchange Capitalization Weighted Stmentioning

confidence: 99%

Forecasting Model Based on Neutrosophic Logical Relationship and Jaccard Similarity

Guan

Zhao

2017

Symmetry

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show abstract

“…In order to compare the forecasting results with other researchers' work [10,11,27,36,[48][49][50][51], the authentic TAIEX (Taiwan Stock Exchange Capitalization Weighted Stock Index) is employed to illustrate the forecasting process. The data from January 1999 to October 1999 are used as training time series and the data from November 1999 to December 1999 are used as testing dataset.…”

Section: A Novel Forecasting Model Based On High-order Fuzzy-fluctuatmentioning

confidence: 99%

“…In order to compare the forecasting results with other researchers' work [10,11,27,36,[48][49][50][51] Step 1: Construct FFTS for historical training data For each element X(t)(t = 1, 2, ..., T) in the historical training time series, its fluctuation trend is determined by Y(t) = X(t) − X(t − 1), (t = 2, 3, ..., T). According to the range and orientation of the fluctuations, Y(t)(t = 2, 3, ..., T) can be fuzzified into a linguistic set {down, equal, up}.…”

Section: A Novel Forecasting Model Based On High-order Fuzzy-fluctuatmentioning

confidence: 99%

Forecasting Based on High-Order Fuzzy-Fluctuation Trends and Particle Swarm Optimization Machine Learning

2017

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Most existing fuzzy forecasting models partition historical training time series into fuzzy time series and build fuzzy-trend logical relationship groups to generate forecasting rules. The determination process of intervals is complex and uncertain. In this paper, we present a novel fuzzy forecasting model based on high-order fuzzy-fluctuation trends and the fuzzy-fluctuation logical relationships of the training time series. Firstly, we compare each piece of data with the data of theprevious day in a historical training time series to generate a new fluctuation trend time series (FTTS). Then, we fuzzify the FTTS into a fuzzy-fluctuation time series (FFTS) according to the up, equal, or down range and orientation of the fluctuations. Since the relationship between historical FFTS and the fluctuation trend of the future is nonlinear, a particle swarm optimization (PSO) algorithm is employed to estimate the proportions for the lagged variables of the fuzzy AR (n) model. Finally, we use the acquired parameters to forecast future fluctuations. In order to compare the performance of the proposed model with that of the other models, we apply the proposed method to forecast the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) time series datasets. The experimental results and the comparison results show that the proposed method can be successfully applied in stock market forecasting or similarkinds of time series. We also apply the proposed method to forecast Shanghai Stock Exchange Composite Index (SHSECI) and DAX30 index to verify its effectiveness and universality.

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“…Jang (1993) and Jang et al (1997) introduced the adaptive network-based fuzzy inference system (AN-FIS), which is a system using a hybrid learning rule to optimize the fuzzy system parameters of a first order Sugeno system (Giovanis 2012). This approach has been applied to a growing range of disciplines, including transport mode choice (Andrade et al 2007), economics (Fang 2012;Giovanis 2012), electricity demand forecasting (Zahedi et al 2013), financial markets forecasting (Bagheri et al 2014;Kablan 2009), gold price forecasting (Makridou et al 2013), oil consumption forecasting (Senvar et al 2013), stock market forecasting (Atsalakis, Valavanis 2009; Chen et al 2013;Cheng et al 2013;Svalina et al 2013;Wei 2013), tourism demand forecasting (Atsalakis et al 2014;Chen et al 2010;Hadavandi et al 2011), and ordering policy in supply chains (Latif et al 2014).…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Neuro-Fuzzy Inference System for Forecasting Australia’s Domestic Low Cost Carrier Passenger Demand

2015

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Abstract. This study has proposed and empirically tested two Adaptive Neuro-Fuzzy Inference System (ANFIS) models for the first time for predicting Australia's domestic low cost carriers' demand, as measured by enplaned passengers (PAX Model) and revenue passenger kilometres performed (RPKs Model). In the ANFIS, both the learning capabilities of an artificial neural network (ANN) and the reasoning capabilities of fuzzy logic are combined to provide enhanced prediction capabilities, as compared to using a single methodology. Sugeno fuzzy rules were used in the ANFIS structure and the Gaussian membership function and linear membership functions were also developed. The hybrid learning algorithm and the subtractive clustering partition method were used to generate the optimum ANFIS models. Data was normalized in order to increase the model's training performance. The results found that the mean absolute percentage error (MAPE) for the overall data set of the PAX and RPKs models was 1.52% and 1.17%, respectively. The highest R 2 -value for the PAX model was 0.9949 and 0.9953 for the RPKs model, demonstrating that the models have high predictive capabilities.

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OWA-based ANFIS model for TAIEX forecasting

Cited by 69 publications

References 30 publications

Forecasting Model Based on Neutrosophic Logical Relationship and Jaccard Similarity

Forecasting Model Based on Neutrosophic Logical Relationship and Jaccard Similarity

Forecasting Based on High-Order Fuzzy-Fluctuation Trends and Particle Swarm Optimization Machine Learning

An Adaptive Neuro-Fuzzy Inference System for Forecasting Australia’s Domestic Low Cost Carrier Passenger Demand

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