A Semiparametric Model for Time Series Based on Fuzzy Data

Hesamian, Gholamreza; Akbari, Mohammad Ghasem

doi:10.1109/tfuzz.2018.2791931

Cited by 13 publications

(16 citation statements)

References 47 publications

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“…The traditional dynamic smoothing of one-dimensional time series is based on the actual series values in the past, so as to reveal the law of linear transformation changing with time. However, it cannot be extended to obtain short-term linear one-dimensional dynamic prediction in the future [6,7]. Although the onedimensional exponential smoothing algorithm can achieve a certain degree of short-term forecasting [8], it ignores the trend and seasonal factors of the original wind power output [9].…”

Section: Introductionmentioning

confidence: 99%

A reliable method of wind power fluctuation smoothing strategy based on multidimensional non‐linear exponential smoothing short‐term forecasting

Zheng

Jin

2022

IET Renewable Power Gen

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This paper investigates the multi‐dimensional non‐linear exponential smoothing prediction method under different weight coefficients, so as to realize the short‐term prediction of the original wind power output. Based on the current research on wind power fluctuation prediction, this paper firstly proposes a new multi‐dimensional nonlinear exponential smoothing prediction method. Based on different weight coefficients, the corresponding short‐term forecast volatility is obtained. Then, the wavelet packet decomposition method is used to further realize the secondary smoothing of the prediction wind power fluctuations. Hybrid energy storage system (HESS) plays an important role in wind power fluctuation suppression from different frequency bands. However, the traditional fixed frequency power allocation method significantly degrades the performance of the accuracy of the algorithm. Therefore, this paper proposes a frequency conversion entropy strategy, which combines energy value with wavelet packet decomposition. By calculating the energy components of different frequency bands, the fluctuation can be divided effectively. Finally, simulation and experiment discussions show that the proposed algorithm in this paper can realize the power allocation effectively compared to the traditional one. And it further verifies the feasibility and reliability of the strategy proposed in this work.

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Section: Introductionmentioning

confidence: 99%

A reliable method of wind power fluctuation smoothing strategy based on multidimensional non‐linear exponential smoothing short‐term forecasting

Zheng

Jin

2022

IET Renewable Power Gen

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“…The PM does not consider nonlinear errors, but the existence of nonlinear errors can also cause pointing errors [12]. In this paper, we add non-parametric components to the PM to establish an SPM [13][14][15]. The PM and the SPM are used to compensate for the original errors.…”

Section: Introductionmentioning

confidence: 99%

Modeling, measurement, and calibration of three-axis integrated aerial camera pointing errors

Líu¹,

Zhou²,

Liu³

et al. 2021

Meas. Sci. Technol.

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Aerial cameras are currently widely used in various fields. However, traditional aerial cameras have certain limitations. We propose a new three-axis integrated aerial camera that can significantly improve imaging efficiency. Both traditional aerial cameras and three-axis integrated aerial cameras suffer from reductions in image quality due to mechanical errors. In this paper, we analyze the influence of mechanical errors on pointing errors and establish a pointing parametric model (PM) based on spatial coordinate transformation and ray tracing. We also consider how the presence of nonlinear errors in aerial cameras affects image quality, and propose a semi-parametric model (SPM) to compensate for nonlinear errors. The PM and the SPM can be used to calibrate the pointing of aerial cameras. In addition, we propose an improved measurement method that can gauge the pointing errors that occur when the three-axis integrated aerial camera rotates to different angles. The results prove that both the PM and the SPM can effectively calibrate the pointing of the aerial camera. Following calibration using the SPM, the pointing errors were greatly reduced. The mean was reduced by more than two orders of magnitude, and the variance was reduced by 99.95%. The SPM completes the pointing calibration better than the PM.

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“…In addition to the abovementioned methods, there are also fuzzy time series models that rely on fuzzy data, but comparatively few overall. In this regard, Hesamian and Akbari [66] first suggested a fuzzy semi-parametric time series model (FSPTSM) based on fuzzy data, non-fuzzy coefficients, and fuzzy smooth functions. Secondly, Zarei et al [67] used a specific variant of the FSPTSM [66] for triangular fuzzy data and different distance measures for fuzzy data.…”

mentioning

confidence: 99%

“…In this regard, Hesamian and Akbari [66] first suggested a fuzzy semi-parametric time series model (FSPTSM) based on fuzzy data, non-fuzzy coefficients, and fuzzy smooth functions. Secondly, Zarei et al [67] used a specific variant of the FSPTSM [66] for triangular fuzzy data and different distance measures for fuzzy data. And thirdly, Hesamian et al [68] introduced a forward additive time series model (FATSM) for fuzzy observations.…”

mentioning

confidence: 99%

A Three-Stage Nonparametric Kernel-Based Time Series Model Based on Fuzzy Data

2023

Self Cite

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In this paper, a nonlinear time series model is developed for the case when the underlying time series data are reported by LR fuzzy numbers. To this end, we present a three-stage nonparametric kernel-based estimation procedure for the center as well as the left and right spreads of the unknown nonlinear fuzzy smooth function. In each stage, the nonparametric Nadaraya–Watson estimator is used to evaluate the center and the spreads of the fuzzy smooth function. A hybrid algorithm is proposed to estimate the unknown optimal bandwidths and autoregressive order simultaneously. Various goodness-of-fit measures are utilized for performance assessment of the fuzzy nonlinear kernel-based time series model and for comparative analysis. The practical applicability and superiority of the novel approach in comparison with further fuzzy time series models are demonstrated via a simulation study and some real-life applications.

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A Semiparametric Model for Time Series Based on Fuzzy Data

Cited by 13 publications

References 47 publications

A reliable method of wind power fluctuation smoothing strategy based on multidimensional non‐linear exponential smoothing short‐term forecasting

A reliable method of wind power fluctuation smoothing strategy based on multidimensional non‐linear exponential smoothing short‐term forecasting

Modeling, measurement, and calibration of three-axis integrated aerial camera pointing errors

A Three-Stage Nonparametric Kernel-Based Time Series Model Based on Fuzzy Data

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