A Modified Weighted Fuzzy Time Series Model for Forecasting Based on Two-Factors Logical Relationship

Abhishekh,; Kumar, Sanjay

doi:10.1007/s40815-019-00652-8

Cited by 18 publications

(8 citation statements)

References 28 publications

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“…Zeng [40] combined the neural network gray model with the TFN to construct the TFGM (1,1) model for prediction. Bilgic [6] combined the optimization algorithm with the TFGM (1,1) model to propose the MFO-TFGM (1,1) model for prediction. Ali [3] introduced a triangular fuzzy membership function in the prediction fuzzy system.…”

Section: Introductionmentioning

confidence: 99%

“…Edendi [12] applied symmetric TFNs in fuzzy random auto-regressive time series model. Abhishekh [1] build a weighted fuzzy time series model based on a two-factor logistic relationship to predict the historical data of the Sensex through symmetric TFNs. Ningrum [22] studied the difference between symmetric TFNs and asymmetric TFNs in prediction, and the final result shows that the prediction error of the asymmetric TFN is not as small as that of the symmetric TFN.…”

Section: Introductionmentioning

confidence: 99%

“…Cases of  in the CTF-GOWA operator In the following, consider the general formulation of the CTF-GOWA operator in the case of special values of  . It takes some special value of  to get different centralized operators including the CTF-OWA operator, the CTF-OWGA operator, the CTF-OWHA operator and the continuous triangular fuzzy ordered weighted quadratic averaging (CTF-OWQA) operator (1)…”

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confidence: 99%

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Continuous Triangular Fuzzy Generalized OWA Operator and Its Application to Combined Prediction

Zhou

Zhang

et al. 2023

Preprint

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In this paper, a new aggregation operator, called the continuous triangular fuzzy generalized ordered weighted averaging (CTF-GOWA) operator, is proposed for aggregating triangular fuzzy numbers (TFNs). In order to adapt to the uncertainty and complexity of prediction, differential polymerization method is used and a special form of CTF-GOWA operator is generated. In particular, the special cases of control parameters and basic unit monotone functions introduced in information aggregation is discussed and its credibility is verified. In addition, the CTF-GOWA operator is applied to the aggregation of multiple TFNs, and various aggregation operators are obtained, including the weighted CTF-GOWA (WCTF-GOWA) operator, the ordered WCTF-GOWA (OWCTF-GOWA) operator, and the combined CTF-GOWA (CCTF-GOWA) operator. Later, the induced variables are added to the OWCTF-GOWA operator to obtain the induced OWCTF-GOWA (IOWCTF-GOWA) operator, which is combined with the cosine similarity degree to construct a combined prediction model about the TFNs. The proposed model has the advantages of less computation, high accuracy, and more adaptability to the actual situation. Finally, the validity of the proposed model is verified by illustrative examples. Sensitivity and comparative analyses are also implemented to assess the stability and validity of our method.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Continuous Triangular Fuzzy Generalized OWA Operator and Its Application to Combined Prediction

Zhou

Zhang

et al. 2023

Preprint

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“…Some literature further studied the fuzzy relation for increasing the accuracy of the forecasting model. Abhishekh [19] researched the fuzzy relations in the forecasting model. Kocak [20] used an ARMA-type recurrent Pi-Sigma artificial neural network to replace fuzzy relations in high-order fuzzy time series.…”

Section: Introducationmentioning

confidence: 99%

Improved seagull optimization algorithm of partition and XGBoost of prediction for fuzzy time series forecasting of COVID-19 daily confirmed

Xian

Chen²,

Cheng³

2022

Advances in Engineering Software

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“…The weighted and polynomial constructions were introduced for each fuzzy logic relationship to assign larger weights to recent time series observations (compared with the latter) [7] or to those with higher empirical probabilities [8]. The fuzzy trend of the forecasted value was also incorporated into the final forecasts [9,10]. The main problem with the construction of traditional fuzzy logic relationships is their poor generalization capacity, this is poor out-of-sample forecasts.…”

Section: Introductionmentioning

confidence: 99%

Intuitionistic Fuzzy Neural Network for Time Series Forecasting - The Case of Metal Prices

Hájek

Olej

Froelich

et al. 2021

IFIP Advances in Information and Communication Technology

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Forecasting time series is an important problem addressed for years. Despite that, it still raises an active interest of researchers. The main issue related to that problem is the inherent uncertainty in data which is hard to be represented in the form of a forecasting model. To solve that issue, a fuzzy model of time series was proposed. Recent developments of that model extend the level of uncertainty involved in data using intuitionistic fuzzy sets. It is, however, worth noting that additional fuzziness exhibits nonlinear behavior. To cope with that issue, we propose a time series model that represents both high uncertainty and non-linearity involved in the data. Specifically, we propose a forecasting model integrating intuitionistic fuzzy sets with neural networks for predicting metal prices. We validate our approach using five financial multivariate time series. The results are compared with those produced by state-of-the-art fuzzy time series models. Thus, we provide solid evidence of high effectiveness of our approach for both one-and five-dayahead forecasting horizons.

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A Modified Weighted Fuzzy Time Series Model for Forecasting Based on Two-Factors Logical Relationship

Cited by 18 publications

References 28 publications

Continuous Triangular Fuzzy Generalized OWA Operator and Its Application to Combined Prediction

Continuous Triangular Fuzzy Generalized OWA Operator and Its Application to Combined Prediction

Improved seagull optimization algorithm of partition and XGBoost of prediction for fuzzy time series forecasting of COVID-19 daily confirmed

Intuitionistic Fuzzy Neural Network for Time Series Forecasting - The Case of Metal Prices

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