Forward Moving Average Representation in Multivariate MA(1) Processes

Mohammadpour, Mehrnaz; Soltani, Aboozar

doi:10.1080/03610920902788095

Cited by 6 publications

(3 citation statements)

References 6 publications

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“…Early time series modeling mainly used statistical methods. Based on the randomness theory of time series, the AR model [26] and MA model [27] are proposed. e AR model uses the correlation between the previous data and the later data to establish a regression equation containing the previous data and the later data.…”

Section: Time Series Prediction Methodmentioning

confidence: 99%

Broad Echo State Network with Reservoir Pruning for Nonstationary Time Series Prediction

Liu

Bai

Wang

et al. 2022

Computational Intelligence and Neuroscience

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The nonstationary time series is generated in various natural and man-made systems, of which the prediction is vital for advanced control and management. The neural networks have been explored in the time series prediction, but the problem remains in modeling the data’s nonstationary and nonlinear features. Referring to the time series feature and network property, a novel network is designed with dynamic optimization of the model structure. Firstly, the echo state network (ESN) is introduced into the broad learning system (BLS). The broad echo state network (BESN) can increase the training efficiency with the incremental learning algorithm by removing the error backpropagation. Secondly, an optimization algorithm is proposed to reduce the redundant information in the training process of BESN units. The number of neurons in BESN with a fixed step size is pruned according to the contribution degree. Finally, the improved network is applied in the different datasets. The tests in the time series of natural and man-made systems prove that the proposed network performs better on the nonstationary time series prediction than the typical methods, including the ESN, BLS, and recurrent neural network.

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Section: Time Series Prediction Methodmentioning

confidence: 99%

Broad Echo State Network with Reservoir Pruning for Nonstationary Time Series Prediction

Liu

Bai

Wang

et al. 2022

Computational Intelligence and Neuroscience

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show abstract

“…The early statistical methods are mainly the AR [20], MA [21], and ARMA [22] models based on the randomness theory. They use the regression equation established by the historical and current data correlation.…”

Section: Related Work 21 Time Series Prediction Methodsmentioning

confidence: 99%

A Novel Broad Echo State Network for Time Series Prediction: Cascade of Mapping Nodes and Optimization of Enhancement Layer

et al. 2022

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Time series prediction is crucial for advanced control and management of complex systems, while the actual data are usually highly nonlinear and nonstationary. A novel broad echo state network is proposed herein for the prediction problem of complex time series data. Firstly, the framework of the broad echo state network with cascade of mapping nodes (CMBESN) is designed by embedding the echo state network units into the broad learning system. Secondly, the number of enhancement layer nodes of the CMBESN is determined by proposing an incremental algorithm. It can obtain the optimal network structure parameters. Meanwhile, an optimization method is proposed based on the nonstationary statistic metrics to determine the enhancement layer. Finally, experiments are conducted both on the simulated and actual datasets. The results show that the proposed CMBESN and its optimization have good prediction capability for nonstationary time series data.

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“…Therefore, it is necessary to introduce the relevant technologies of big data analysis to perform necessary processing on the outliers and missing values, so as to improve the quality of the original data. For the accidental error of the original data [45], [46], moving average processing is performed to eliminate the error [47], [48].…”

Section: ) Mass Concrete Temperature Datamentioning

confidence: 99%

Research on CART Model of Mass Concrete Temperature Prediction Based on Big Data Processing Technology

Li²,

Wang³

et al. 2022

IEEE Access

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Due to the influence of temperature changes or temperature gradients in the construction process of mass concrete, temperature cracks will occur in the concrete. In order to achieve a reasonable prediction of the temperature change of the mass concrete during the construction process and accurately obtain the temperature change trend, this paper attempts to construct a CART prediction model based on the big data processing technology based on the characteristics of the temperature change of the mass concrete. This paper introduces in detail how to use data processing methods such as outlier identification, missing value filling and random error elimination to improve data quality, as well as the method for constructing the CART prediction model, and combines engineering examples to demonstrate the feasibility of the model method. The results show that the model and method can better predict the temperature change of mass concrete. It has high prediction accuracy and can provide necessary guidance for practical engineering.INDEX TERMS Mass concrete temperature, big data processing technology, CART prediction model.

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Forward Moving Average Representation in Multivariate MA(1) Processes

Cited by 6 publications

References 6 publications

Broad Echo State Network with Reservoir Pruning for Nonstationary Time Series Prediction

Broad Echo State Network with Reservoir Pruning for Nonstationary Time Series Prediction

A Novel Broad Echo State Network for Time Series Prediction: Cascade of Mapping Nodes and Optimization of Enhancement Layer

Research on CART Model of Mass Concrete Temperature Prediction Based on Big Data Processing Technology

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