Time series forecasting with interval type-2 intuitionistic fuzzy logic systems

Eyoh, Imo; John, Robert; Maere, Geert De

doi:10.1109/fuzz-ieee.2017.8015463

Cited by 25 publications

(9 citation statements)

References 33 publications

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“…The proposed model is applied to non-linear system prediction with good results. The same model is also applied to time series prediction [8].…”

Section: Related Workmentioning

confidence: 99%

Untitled

2020

IJFLS

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This paper proposes a sliding mode control-based learning of interval type-2 intuitionistic fuzzy logic system for time series and identification problems. Until now, derivative-based algorithms such as gradient descent back propagation, extended Kalman filter, decoupled extended Kalman filter and hybrid method of decoupled extended Kalman filter and gradient descent methods have been utilized for the optimization of the parameters of interval type-2 intuitionistic fuzzy logic systems. The proposed model is based on a Takagi-Sugeno-Kang inference system. The evaluations of the model are conducted using both real world and artificially generated datasets. Analysis of results reveals that the proposed interval type-2 intuitionistic fuzzy logic system trained with sliding mode control learning algorithm (derivative-free) do outperforms some existing models in terms of the test root mean squared error while competing favourable with other models in the literature. Moreover, the proposed model may stand as a good choice for real time applications where running time is paramount compared to the derivative-based models. KEYWORDSInterval type-2 intuitionistic fuzzy set, Sliding mode control algorithm, Intuitionistic fuzzy set, Intuitionistic fuzzy index.

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“…The proposed model is applied to non-linear system prediction with good results. The same model is also applied to time series prediction [8].…”

Section: Related Workmentioning

confidence: 99%

Untitled

2020

IJFLS

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“…Some key features distinguish the Fuzzy Time Series: simplicity and scalability [6]. The use of fuzzy logic has been applied in the literature for time series forecasting and provides a significant improvement over the traditional statistical methods because it is non-linear and it is able to approximate any complex dynamical systems better than linear statistical models [7]. In academic enrollments, Preetika Saxena and Santhosh Easo [8] proposed a method based on fuzzy time series, which gives the higher forecasting accuracy rate than the existing methods.…”

Section: Fuzzy Time Series Forecastingmentioning

confidence: 99%

“…We drive a simple Fuzzy time series prediction example from [7] as follows: given X, a Numerical Variable, X ∈ℝ -for instance an height measure -its Universe of Discourse, abbreviated to U, such that (U = [ min(X), max(X) ]). We define (U = [20,220]) and the linguistic variable Ã as: (Ã = {"very small", "small", "short", "medium", "tall", "very tall"} ) or when we use a 10 partitions scheme, (Ã = {A0, A1, , A9 }).…”

Section: Fuzzy Time Series Representationmentioning

confidence: 99%

Application of Weighted Fuzzy Time Series Model to Forecast Epidemic Injuries

Moneim

2020

CJAST

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Aims: It is important to predict the amount of COVID-19 injuries. Since the first suspected case of novel coronavirus (2019-nCoV) on December 1st, 2019, in Wuhan, Hubei Province, China, a total of 40,235 confirmed cases and 909 deaths have been reported in China up to February 10, 2020, evoking fear locally and internationally. Here, based on the large amounts of daily publicly available epidemiological data and the need to make an accurate prediction of future behavior requires the definition of powerful and effective techniques capable of inferring random dependency between the past and the future from observations. In this paper, we apply a rewarding model to predict injuries in areas where COVID-19 is, especially in the Arab region. This forecast uses epidemic injuries data from March 2nd, 2020 to July 20th, 2020 in Saudi Arabia. Methodology: We propose the use of weighted fuzzy time series techniques (WFTS) and weighted non-stationary fuzzy time series techniques (WNSFTS) to be compared with the classical Auto-Regressive Integrated Moving Average (ARIMA) statistical method. The available data is not a stationary and should therefore be converted first to stationary to forecast it with (ARIMA) and (WFTS) techniques. We do a log transform and differencing on our injuries dataset. Results: When we examine the original data by Dickey-Fuller Test (DFT) to get p-value, we find it is equal to 0.646, it is more than 0.05 which implies the non-stationarity. The mean square error (MSE), the root mean square error (RMSE) and normalization root mean square error (NRMSE), are applied to compare the accuracy of the methods. The results show that WFTS methods give good services for predicting epidemic injuries in the territory by COVID-19. Conclusion: The use of Weighted Non Stationary Fuzzy Time Series (WNSFTS) in forecasting epidemic injuries problem can provide significantly better results because it is able to predict the infected cases at the next time and achieve great predictive accuracy.

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“…As an extension of IFSs, Eyoh et al [36] combined the concept of intuitionistic fuzzy sets with the interval type-2 fuzzy logic, and proposed a novel prediction method. Subsequently, in [37], an interval type-2 intuitionistic fuzzy logic system of Takagi-Sugeno-Kang (IT2IFLS-TSK) was proposed to achieve time series prediction, which utilized more parameters than the classic type-2 fuzzy model. Furthermore, the decoupling extended Kalman filter (DEKF) was also used to optimize the parameters of IT2IFLS-TSK [38].…”

Section: Introductionmentioning

confidence: 99%

Online Evolving Interval Type-2 Intuitionistic Fuzzy LSTM-Neural Networks for Regression Problems

Wang

Luo

2019

IEEE Access

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Since the existence of fuzziness in the real world, uncertainty could be always present in the reasoning results. Therefore, how to tackle with the hesitation existing in the process of reasoning is a meaningful issue for the construction of the inference system. In this paper, a novel interval type-2 intuitionistic fuzzy neural network based on long-short term mechanism is proposed (LSTM-IT2IFNN). By means of interval type-2 intuitionistic set, the hesitation of reasoning is described and involved to determine fuzzy rules in inference system, by which the chosen of fuzzy rules not only considers the membership value but also non-membership degree. In order to handle the regression problems with long-term time dependency, long-short term mechanism is introduced into fuzzy neural networks, which can effectively improve the memory ability of long-term knowledge. Furthermore, by means of intuitionistic sets, an extended metacognitive learning mechanism is proposed for the adaptively online learning of network structure, where it achieves when, what, and how to learn from the sequential data. The performance of the LSTM-IT2IFNN is evaluated on different regression problems, including synthetic and real-world data. By comparing to existing algorithms, the experimental results show the efficiency and accuracy of the proposed scheme. INDEX TERMS Fuzzy neural network, intuitionistic fuzzy sets, interval type-2 fuzzy systems, metacognition, sequential learning.

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Time series forecasting with interval type-2 intuitionistic fuzzy logic systems

Cited by 25 publications

References 33 publications

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Application of Weighted Fuzzy Time Series Model to Forecast Epidemic Injuries

Online Evolving Interval Type-2 Intuitionistic Fuzzy LSTM-Neural Networks for Regression Problems

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