ArticleEntropy (Basel, Switzerland)2022
Deep Prediction Model Based on Dual Decomposition with Entropy and Frequency Statistics for Nonstationary Time Series.
Article in Entropy (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Prediction Algorithm of Uncertain Fund Demand for Regional Economics Using GM Model and Few-Shot Learning.Computational intelligence and neuroscience · 2022Article
- Optimization of Vocal Singing Training Methods Using Intelligent Big Data Technology.Computational intelligence and neuroscience · 2022Article
- An Intelligent and Fast Dance Action Recognition Model Using Two-Dimensional Convolution Network Method.Journal of environmental and public health · 2022Article
- Prediction Model of Piano Collective Class Teaching and Learning Effect Based on Deep Learning.Computational intelligence and neuroscience · 2022Article
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Authors and funding
6 authors.
Funding
Abstract
The prediction of time series is of great significance for rational planning and risk prevention. However, time series data in various natural and artificial systems are nonstationary and complex, which makes them difficult to predict. An improved deep prediction method is proposed herein based on the dual variational mode decomposition of a nonstationary time series. First, criteria were determined based on information entropy and frequency statistics to determine the quantity of components in the variational mode decomposition, including the number of subsequences and the conditions for dual decomposition. Second, a deep prediction model was built for the subsequences obtained after the dual decomposition. Third, a general framework was proposed to integrate the data decomposition and deep prediction models. The method was verified on practical time series data with some contrast methods. The results show that it performed better than single deep network and traditional decomposition methods. The proposed method can effectively extract the characteristics of a nonstationary time series and obtain reliable prediction results.
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Registered trials
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