A Hybrid Linguistic Time Series Forecasting Model combined with Particle Swarm Optimization

Phong, Phạm Đình; Hieu, Nguyen Duy; Linh, Mai Van

doi:10.1109/icecet55527.2022.9873100

Cited by 2 publications

(2 citation statements)

References 20 publications

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“…To show the efficiency of the application of PSO-SA, our proposed FTS-FMs with the application of different defuzzification techniques are compared with the FTS-FM proposed by Chen&Zou in [27], Uslu in [32], and linguistic time series (LTS) proposed by Phong in [33]. The comparison results are shown in Table 4 and visualized in Figure 1.…”

Section: 1mentioning

confidence: 99%

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A Hybrid Pso-Sa Scheme for Improving Accuracy of Fuzzy Time Series Forecasting Models

Phong

Dư

Hiệp

et al. 2022

JCC

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Forecasting methods based on fuzzy time series have been examined intensively during last years. Three main factors which affect the accuracy of those forecasting methods are length of intervals, the way of establishing fuzzy logical relationship groups, and defuzzification techniques. Many researches focus on optimizing length of intervals in order to improve forecasting accuracies by utilizing various optimization techniques. In the line of that research trend, in this paper, a hybrid particle swarm optimization combined with simulated annealing (PSO-SA) algorithm is proposed to optimize length of intervals to improve forecasting accuracies. The experimental results in comparison with the existing forecasting models show that the proposed forecasting model is an effective forecasting model.

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Section: 1mentioning

confidence: 99%

“…The difference between M9 and Chen&Zou's model is only the optimization algorithm. In [33], Phong et al applied PSO to optimize the fuzziness parameter values of the LTS forecasting model. The MSE of it is better than those of Chen&Zou and Uslu, but worse than ours.…”

Section: 1mentioning

confidence: 99%

A Hybrid Pso-Sa Scheme for Improving Accuracy of Fuzzy Time Series Forecasting Models

Phong

Dư

Hiệp

et al. 2022

JCC

View full text Add to dashboard Cite

show abstract

A Co-Optimization Algorithm Utilizing Particle Swarm Optimization for Linguistic Time Series

2023

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The linguistic time-series forecasting model (LTS-FM), which has been recently proposed, uses linguistic words of linguistic variable domains generated by hedge algebras (HAs) to describe historical numeric time-series data. Then, the LTS-FM was established by utilizing real numeric semantics of words induced by the fuzziness parameter values (FPVs) of HAs. In the existing LTS-FMs, just the FPVs of HAs are optimized, while the used word set is still chosen by human experts. This paper proposes a co-optimization method of selecting the optimal used word set that best describes numeric time-series data in parallel with choosing the best FPVs of HAs to improve the accuracy of LTS-FMs by utilizing particle swarm optimization (PSO). In this co-optimization method, the outer loop optimizes the FPVs of HAs, while the inner loop optimizes the used word set. The experimental results on three datasets, i.e., the “enrollments of the University of Alabama” (EUA), the “killed in car road accidents in Belgium” (CAB), and the “spot gold in Turkey” (SGT), showed that our proposed forecasting model outperformed the existing forecasting models in terms of forecast accuracy.

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A Hybrid Linguistic Time Series Forecasting Model combined with Particle Swarm Optimization

Cited by 2 publications

References 20 publications

A Hybrid Pso-Sa Scheme for Improving Accuracy of Fuzzy Time Series Forecasting Models

A Hybrid Pso-Sa Scheme for Improving Accuracy of Fuzzy Time Series Forecasting Models

A Co-Optimization Algorithm Utilizing Particle Swarm Optimization for Linguistic Time Series

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