Method of pulse function fitting based on the corona current measurement data

Hu, Xin; Zhang, Yue; Wang, Lei; Hao, Lei

doi:10.1109/ceem.2015.7368714

Cited by 4 publications

(2 citation statements)

References 9 publications

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“…The predicted probability density function, and K represents the number of set members. It is the posterior probability when the k-th member is k w the best prediction, and the sum of the posterior probabilities is 1, which can also be called the weight [10]. As indicated (5) by the equation, y the posterior distribution of ( |Z) k Pf is actually a weighted average of the posterior distributions of all models, with the posterior probabilities serving as weights.…”

Section: Bayesian Model Averagingmentioning

confidence: 99%

High-Frequency Time Series Data Regression Based on the Functional Bayesian Model Averaging

Cui¹,

Yang²,

Chen³

2023

HSET

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Regression analysis of high-frequency time series data has been an important problem in the field of statistical learning. In this paper, we propose a novel approach based on multiple basis function expansions and Bayesian model averaging when the predictor variable is a high-frequency time series and the response variable is continuous scalar data. On the one hand, the proposed method avoids the curse of dimensionality and extracts the functional information of the original sequence by transforming the discrete data into continuous functions in a function space consisting of some fixed basis functions. On the other hand, for the choice of basis functions, this paper proposes to adaptively determine the optimal solution using Bayesian model averaging, effectively balancing the variance and bias of the predictive model. The real data analysis shows that the proposed method has smaller mean squared error and absolute error, and is robust compared to other methods. Finally, the proposed method can be further extended to time series data applications such as weather forecasting and stock price prediction.

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Section: Bayesian Model Averagingmentioning

confidence: 99%

High-Frequency Time Series Data Regression Based on the Functional Bayesian Model Averaging

Cui¹,

Yang²,

Chen³

2023

HSET

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“…Ray and Mallick [11] developed a non-parametric Bayesian analysis framework. Chen [12] used basis function expansion for function-based data clustering and solved the problem of the Pearson similarity coefficient being unable to describe differences in curve shape due to Euclidean distance. Du et al [13] proposed an innovative functional data clustering method based on directional multiple hypothesis test and information entropy.…”

Section: Introductionmentioning

confidence: 99%

Functional Data Clustering Via Functional Mahalanobis Distance

Zao

2023

HSET

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As an exploratory data analysis method, functional data clustering aims to identify the underlying features of the observed data. In this context, this paper proposes a functional data clustering method based on functional Mahalanobis distance. As a distance-based non-parametric clustering model, the proposed method can effectively avoid the disadvantages of generative models and has excellent properties of decoupling and dimension standardization. Compared with other functional data clustering models, this method has lower computational complexity. In addition, the proposed method can be applied to any distance-based multivariate clustering method, thus generalizing it to the case of functional data. In practical data analysis, this paper compares the performance of this method with some other functional clustering methods, using k-means clustering as an example, and finds that it has better performance in terms of purity, adjusted Rand index, and computational speed. Finally, the idea of using Mahalanobis distance for functional data distance measurement can also be extended to construct kernel functions for measuring similarity between functional data samples, thus developing non-linear functional data analysis methods based on reproducing kernel theory.

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One Method to Fit Biexponential Curve by Using Fourier Transform

Wang

et al. 2018

2018 IEEE 3rd International Conference on Integrated Circuits and Microsystems (ICICM)

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Method of pulse function fitting based on the corona current measurement data

Cited by 4 publications

References 9 publications

High-Frequency Time Series Data Regression Based on the Functional Bayesian Model Averaging

High-Frequency Time Series Data Regression Based on the Functional Bayesian Model Averaging

Functional Data Clustering Via Functional Mahalanobis Distance

One Method to Fit Biexponential Curve by Using Fourier Transform

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