Forecasting in Fuzzy Time Series by an Extension of Simple Exponential Smoothing

Santos, Fábio José Justo dos; Camargo, Heloisa de Arruda

doi:10.1007/978-3-319-12027-0_21

Cited by 3 publications

(3 citation statements)

References 19 publications

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“…When the knowledge base does not have at least one equivalent FLR to be used in the process of forecasting, some forecast methods use the centroid of the last linguistic term in the FTS as the forecasted value [8,13,14]. Aiming at increasing the accuracy in forecasting in these cases, in the proposed model the use of simple linear regression combined with FTS is introduced.…”

Section: Forecasting Modelmentioning

confidence: 99%

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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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Section: Forecasting Modelmentioning

confidence: 99%

“…Towards this line of investigation, the authors of the present work have proposed recently an approach that combines FTS with simple exponential smoothing (SES) [8]. In this paper the method has been extended in several ways.…”

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

Self Cite

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show abstract

“…With the development of applications and the continuous exploration of researchers, the traditional method has become more mature, and it is also one of the most commonly used methods in time series forecasting. For instance, classical statistical methods (e.g., auto-regressive integrated moving average model (ARIMA) [4], hidden Markov models (HMMs) [5], exponential smoothing [6], etc. ), have made great progress.…”

Section: Introductionmentioning

confidence: 99%

A Combined Model for Multivariate Time Series Forecasting Based on MLP-Feedforward Attention-LSTM

2022

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Multivariate time series forecasting has very great practical significance for a long time, and it has been attracting the attention of researchers from a diverse range of fields. However, it is difficult to analyze the relationship and transformation law among multivariate data. Further, it is hard to obtain a relatively accurate prediction. In recent years, long short-term memory (LSTM) has shown high capability in dealing with nonlinearity and long memory of time series data. Although LSTM can also process multivariate data, it is insufficient to pay various degrees of attention to multivariate data. To address this issue, a multivariate time series prediction model based on multilayer perceptron (MLP), feedforward attention mechanism, and LSTM is proposed in this paper. Firstly, the simulation process utilizes the MLP module to map the multivariate initial sequences into another latent dimensional space, thereby obtaining easily captured mapping features. Then, these features are adaptively assigned attention weights through the feed-forward attention mechanism. Finally, the LSTM module uses these feature sequences with attention weights to make final predictions. The experimental results show that the method that combines the MLP layer with the feed-forward attention layer is effective in extracting multivariate features. Also, the empirical results indicate that our proposed framework (a combined model of MLP-Feedforward attention-LSTM) can achieve better performance than baselines.INDEX TERMS Multivariate time series, Multilayer perceptron, Feed-forward attention mechanism, Long short-term memory network

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Integration of 4253HT Smoother with Intuitionistic Fuzzy Time Series Forecasting Model

Alam¹,

Ramli²,

Mohamed³

et al. 2022

Pak.j.stat.oper.res.

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Fuzzy time series is widely used in forecasting time series data in linguistic forms. Implementing the intuitionistic fuzzy sets (IFS) in fuzzy time series can better handle uncertainties and vagueness in the time series data. However, the time series data always fluctuate randomly and cause drastic changes. In this study, the 4253HT smoother is integrated with the intuitionistic fuzzy time series forecasting model to improve the forecasting accuracy. The proposed model is implemented in predicting the Malaysian crude palm oil prices. The data are firstly smoothed, and followed with the fuzzification process. Next are the transformation of fuzzy sets into IFS and the de-i-fuzzification via equal distribution of hesitancy. The forecasted data are calculated based on the defuzzified values considering the new membership degrees of the IFS after de-i-fuzzification. The results show that the integrated model produces a better forecasting performance compared to the common intuitionistic fuzzy time series forecasting model. In the future, the integration of the data smoothing should be considered before the forecasting of data using fuzzy time series could be performed.

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Forecasting in Fuzzy Time Series by an Extension of Simple Exponential Smoothing

Cited by 3 publications

References 19 publications

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

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

A Combined Model for Multivariate Time Series Forecasting Based on MLP-Feedforward Attention-LSTM

Integration of 4253HT Smoother with Intuitionistic Fuzzy Time Series Forecasting Model

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