Interval prediction of short-term traffic speed with limited data input: Application of fuzzy-grey combined prediction model

Song, Zhanguo; Feng, Wei Feng; Liu, Weiwei

doi:10.1016/j.eswa.2021.115878

Cited by 23 publications

(1 citation statement)

References 39 publications

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“…[25] introduced two GRU prediction modules and utilized the PSO method to assign weights to the prediction errors of each sub-signal, resulting in a high-quality stacked prediction interval. Moreover, other scholars have employed different methods to establish prediction intervals [26,27]. In Ref.…”

Section: Introductionmentioning

confidence: 99%

A Novel Twin Support Vector Regression Model for Wind Speed Time-Series Interval Prediction

Feng

Yao

et al. 2023

Energies

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Although the machine-learning model demonstrates high accuracy in wind speed prediction, it struggles to accurately depict the fluctuation range of the predicted values due to the inherent uncertainty in wind speed sequences. To address this limitation and enhance the reliability, we propose an effective wind speed interval prediction model that combines twin support vector regression (TSVR), variational mode decomposition (VMD), and the slime mould algorithm (SMA). In our methodology, the complex wind speed series is decomposed into multiple relatively stable subsequences using the VMD method. The principal component and residual series are then subject to interval prediction using the TSVR model, while the remaining components undergo point prediction. The SMA method is employed to search for optimal parameter combinations. The prediction interval of wind speed is obtained by aggregating the forecasting results of all TSVR models for each subseries. Our proposed model has demonstrated superior performance in various applications. It ensures that the wind speed value falls within the designated interval range while achieving the narrowest prediction interval. For instance, in the spring dataset with 1-period, we obtained a predicted interval with a prediction intervals coverage probability (PICP) value of 0.9791 and prediction interval normalized range width (PINRW) value of 0.0641. This outperforms other comparative models and significantly enhances its practical application value. After adding the residual interval prediction model, the reliability of the prediction interval is significantly improved. As a result, this study presents a novel twin support vector regression model as a valuable approach for multi-step wind speed interval prediction.

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Section: Introductionmentioning

confidence: 99%