A comparison study between fuzzy time series model and ARIMA model for forecasting Taiwan export

Wang, Chi-Chen

doi:10.1016/j.eswa.2011.01.015

Cited by 58 publications

(26 citation statements)

References 10 publications

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“…Other statistics models are regression method, exponential smoothing, generalized autoregressive conditional heteroskedasticity (GARCH). Few related works that has engaged ARIMA model for forecasting includes [11,12,13,14,15,16].…”

Section: Introductionmentioning

confidence: 99%

Stock Price Prediction Using the ARIMA Model

Adebiyi

Adediran

Ayo

2014

2014 UKSim-AMSS 16th International Conference on Computer Modelling and Simulation

697

233

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Stock price prediction is an important topic in finance and economics which has spurred the interest of researchers over the years to develop better predictive models. The autoregressive integrated moving average (ARIMA) models have been explored in literature for time series prediction. This paper presents extensive process of building stock price predictive model using the ARIMA model. Published stock data obtained from New York Stock Exchange (NYSE) and Nigeria Stock Exchange (NSE) are used with stock price predictive model developed. Results obtained revealed that the ARIMA model has a strong potential for short-term prediction and can compete favourably with existing techniques for stock price prediction.

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Section: Introductionmentioning

confidence: 99%

Stock Price Prediction Using the ARIMA Model

Adebiyi

Adediran

Ayo

2014

2014 UKSim-AMSS 16th International Conference on Computer Modelling and Simulation

697

233

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“…Samanta (2011) and Zhu et al (2011) developed methods based on cooperative particle swarm optimization. Works like Qiu et al (2011) and Wang (2011) proposed fuzzy time series models for forecasting, and Yu and Huarng (2010) applied ANNs for training and forecasting in their fuzzy time series model. Models such as support vector regression (Kavaklioglu 2011) and fuzzy expert system (Dash et al 1995) were proposed for the electricity demand forecasting, among others.…”

Section: Soft Computing Methods For Time Series Forecastingmentioning

confidence: 99%

Coevolution of lags and RBFNs for time series forecasting: L-Co-R algorithm

et al. 2011

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This paper introduces Lags COevolving with Rbfns (L-Co-R), a coevolutionary method developed to face time-series forecasting problems. L-Co-R simultaneously evolves the model that provides the forecasted values and the set of time lags the model must use in the prediction process. Coevolution takes place by means of two populations that evolve at the same time, cooperating between them; the first population is composed of radial basis function neural networks; the second one contains the individuals representing the sets of lags. Thus, the final solution provided by the method comprises both the neural net and the set of lags that better approximate the time series. The method has been tested across 34 different time series datasets, and the results compared to 6 different methods referenced in literature, and with respect to 4 different error measures. The results show that L-Co-R outperforms the rest of methods, as the statistical analysis carried out indicates.

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“…On one side, the advantages of traditional statistical forecasting methods are extensively pointed out. On the other side, some works have reported better accuracy by the FTS methods than traditional statistical methods, especially in time series with few samples or with irregular behavior [6,7].…”

Section: Introductionmentioning

confidence: 99%

A hybrid forecast model combining fuzzy time series, linear regression and a new smoothing technique

Santos¹,

Camargo²

2015

Proceedings of the 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and

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In recent years, Fuzzy Time Series have been considered a promising tool to deal with forecasting problems due to the ease to model the problems, the satisfactory results obtained and also to the low computational cost required. However, the long experience with traditional methods coming from statistics, certainly brings a rich knowledge that can be used to enhance the computational methods employed to deal with Fuzzy Time Series. This paper introduces a forecast model where Fuzzy Time Series, linear regression and a new smoothing method are combined. Experiments were performed with the Taiwan Stock Exchange index and compared with eight others approaches found in the literature. The results confirm that the proposed model presents a good accuracy with relation to the other methods.

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A comparison study between fuzzy time series model and ARIMA model for forecasting Taiwan export

Cited by 58 publications

References 10 publications

Stock Price Prediction Using the ARIMA Model

Stock Price Prediction Using the ARIMA Model

Coevolution of lags and RBFNs for time series forecasting: L-Co-R algorithm

A hybrid forecast model combining fuzzy time series, linear regression and a new smoothing technique

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