Reconstructing signal from quantized signal based on singular spectral analysis

Kuang, Weichao; Ling, Bingo Wing‐Kuen; Yang, Zhijing

doi:10.1016/j.dsp.2018.07.019

Cited by 4 publications

(2 citation statements)

References 23 publications

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“…At the same time, machine learning models are suitable for the accurate estimation of complex nonlinear relationships. Therefore, inspired by the success of this hybrid prediction model, an integrated model is proposed, which is based on singular spectrum analysis (SSA), ARIMA, − Prophet, and wavelet neural network (WNN), , for predicting daily oil production. The proposed method consists of the following main steps.…”

Section: Introductionmentioning

confidence: 99%

Research on the Application of Integrated Learning Models in Oilfield Production Forecasting

Ni,

Xin,

et al. 2023

ACS Omega

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Forecasting oil production is crucially important in oilfield management. Currently, multifeature-based modeling methods are widely used, but such modeling methods are not universally applicable due to the different actual conditions of oilfields in different places. In this paper, a time series forecasting method based on an integrated learning model is proposed, which combines the advantages of linearity and nonlinearity and is only concerned with the internal characteristics of the production curve itself, without considering other factors. The method includes processing the production history data using singular spectrum analysis, training the autoregressive integrated moving average model and Prophet, training the wavelet neural network, and forecasting oil production. The method is validated using historical production data from the J oilfield in China from 2011 to 2021, and compared with single models, Arps model, and mainstream time series forecasting models. The results show that in the early prediction, the difference in prediction error between the integrated learning model and other models is not obvious, but in the late prediction, the integrated model still predicts stably and the other models compared with it will show more obvious fluctuations. Therefore, the model in this article can make stable and accurate predictions.

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Section: Introductionmentioning

confidence: 99%

Research on the Application of Integrated Learning Models in Oilfield Production Forecasting

Ni,

Xin,

et al. 2023

ACS Omega

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“…The signals are represented as the sum of the one dimensional singular spectrum analysis vectors [1]. Based on the rationale of judiciously selecting one dimensional singular analysis vectors, successful applications [22] include signal denoising [2]- [5], underlying trend extraction [6], pattern recognition [7] and peak detection [12]. However, unlike the conventional linear and nonadaptive time frequency analysis such as the maximally decimated filter bank analysis [8], [9], the singular spectrum analysis is a kind of oversampled time frequency analysis [1].…”

Section: Introductionmentioning

confidence: 99%

Length Reduction of Singular Spectrum Analysis With Guarantee Exact Perfect Reconstruction via Block Sliding Approach

Wang

Ling

2020

IEEE Access

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The conventional singular spectrum analysis is to divide a signal into segments where there is only one non-overlapping point between two consecutive segments. By putting these segments into the columns of a matrix and performing the singular value decomposition on the matrix, various one dimensional singular spectrum analysis vectors are obtained. Since different one dimensional singular spectrum analysis vectors represent different parts of the signal such as the trend part, the oscillation part and the noise part of the signal, the singular spectrum analysis plays a very important role in the nonlinear and adaptive signal analysis. However, as the length of each one dimensional singular spectrum analysis vector is the same as that of the original signal, there is a redundancy among these one dimensional singular spectrum analysis vectors. In order to reduce the required computational power and the required units for the memory storage for performing the singular spectrum analysis, this paper proposes a method to reduce the total number of the elements of all the one dimensional singular spectrum analysis vectors. In particular, the length of the shift block for generating the trajectory matrix is increased from one to a positive integer greater than one under a certain criterion. In this case, the total number of the columns of the trajectory matrix is reduced. As a result, the total number of the off-diagonals of all the two dimensional singular spectrum analysis matrices is reduced. Hence, the total number of the elements of all the one dimensional singular spectrum analysis vectors is reduced. In order to guarantee exact perfect reconstruction, this paper reformulates the de-Hankelization process. In particular, the first element of the off-diagonals of all the two dimensional singular spectrum analysis matrices is taken as the elements of the one dimensional singular spectrum analysis vectors. Exact perfect reconstruction condition is derived. Simulations show that exact perfect reconstruction can be achieved while the total number of the elements of all the one dimensional singular spectrum analysis vectors is significantly reduced.

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Performing fractional delay via fractional singular spectrum analysis

Zhou

Ling

Huang

et al. 2021

SIViP

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This paper proposes a fractional singular spectrum analysis (SSA) based method for performing the fractional delay. First, the input sequence is divided into two overlapping sequences with the first sequence being the input sequence without its last point and the second sequence being the input sequence without its first point. Then, the singular value decompositions (SVD) are performed on the trajectory matrices constructed based on these two sequences. Next, the designs of both the right unitary matrix and the left unitary matrix for generating the new trajectory matrix are formulated as the quadratically constrained quadratic programing problems. The analytical solutions of these quadratically constrained quadratic programing problems are derived via the SVD approach. Finally, the fractional SSA components are obtained by performing the diagonal averaging operation and the fractional delay sequence is obtained by summing up all the fractional SSA components together. Since the fractional SSA operations are nonlinear and adaptive, our proposed method is a kind of nonlinear and adaptive approach for performing the fractional delay. Besides, by discarding some fractional SSA components, the joint fractional delay operation and the denoising operation can be performed simultaneously.

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Reconstructing signal from quantized signal based on singular spectral analysis

Cited by 4 publications

References 23 publications

Research on the Application of Integrated Learning Models in Oilfield Production Forecasting

Research on the Application of Integrated Learning Models in Oilfield Production Forecasting

Length Reduction of Singular Spectrum Analysis With Guarantee Exact Perfect Reconstruction via Block Sliding Approach

Performing fractional delay via fractional singular spectrum analysis

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