Non-linear Time Series Prediction using Improved CEEMDAN, SVD and LSTM

IF 2.6 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Sameer Poongadan, M. C. Lineesh
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引用次数: 0

Abstract

This study recommends a new time series forecasting model, namely ICEEMDAN - SVD - LSTM model, which coalesces Improved Complete Ensemble EMD with Adaptive Noise, Singular Value Decomposition and Long Short Term Memory network. It can be applied to analyse Non-linear and non-stationary data. The framework of this model is comprised of three levels, namely ICEEMDAN level, SVD level and LSTM level. The first level utilized ICEEMDAN to break up the series into some IMF components along with a residue. The SVD in the second level accounts for de-noising of every IMF component and residue. LSTM forecasts all the resultant IMF components and residue in third level. To obtain the forecasted values of the original data, the predictions of all IMF components and residue are added. The proposed model is contrasted with other extant ones, namely LSTM model, EMD - LSTM model, EEMD - LSTM model, CEEMDAN - LSTM model, EEMD - SVD - LSTM model, ICEEMDAN - LSTM model and CEEMDAN - SVD - LSTM model. The comparison bears witness to the potential of the recommended model over the traditional models.

Abstract Image

利用改进的 CEEMDAN、SVD 和 LSTM 进行非线性时间序列预测
本研究推荐了一种新的时间序列预测模型,即 ICEEMDAN - SVD - LSTM 模型,它将改进的完整集合 EMD 与自适应噪声、奇异值分解和长短期记忆网络结合在一起。它可用于分析非线性和非平稳数据。该模型的框架包括三个层次,即 ICEEMDAN 层次、SVD 层次和 LSTM 层次。第一级利用 ICEEMDAN 将序列分解为一些 IMF 成分和残差。第二级中的 SVD 对每个 IMF 分量和残差进行去噪处理。在第三级中,LSTM 对所有产生的 IMF 分量和残差进行预测。为了获得原始数据的预测值,需要将所有 IMF 分量和残差的预测值相加。建议的模型与其他现有模型进行了对比,即 LSTM 模型、EMD - LSTM 模型、EEMD - LSTM 模型、CEEMDAN - LSTM 模型、EEMD - SVD - LSTM 模型、ICEEMDAN - LSTM 模型和 CEEMDAN - SVD - LSTM 模型。对比结果证明了推荐模型比传统模型更有潜力。
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来源期刊
Neural Processing Letters
Neural Processing Letters 工程技术-计算机:人工智能
CiteScore
4.90
自引率
12.90%
发文量
392
审稿时长
2.8 months
期刊介绍: Neural Processing Letters is an international journal publishing research results and innovative ideas on all aspects of artificial neural networks. Coverage includes theoretical developments, biological models, new formal modes, learning, applications, software and hardware developments, and prospective researches. The journal promotes fast exchange of information in the community of neural network researchers and users. The resurgence of interest in the field of artificial neural networks since the beginning of the 1980s is coupled to tremendous research activity in specialized or multidisciplinary groups. Research, however, is not possible without good communication between people and the exchange of information, especially in a field covering such different areas; fast communication is also a key aspect, and this is the reason for Neural Processing Letters
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