A New Approach to Empirical Mode Decomposition Based on Akima Spline Interpolation Technique

Ali, Muhammad; Khan, Dost Muhammad; Saeed, Imran; Alshanbari, Huda M.

doi:10.1109/access.2023.3253279

Cited by 8 publications

(5 citation statements)

References 35 publications

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“…In this paper, our preprocessing of TEC and Dst data only includes differentiation. In the future, we will do more preprocessing on the data, such as a new empirical mode decomposition method based on Akima spline interpolation technology [56], to further improve the performance of the model. The model in this paper sacrifices memory footprint for better prediction performance, and in the future, we will investigate lightweight models that reduce computational requirements without compromising accuracy.…”

Section: Discussionmentioning

confidence: 99%

TEC Prediction Based on Att-CNN-BiLSTM

Liu,

Wang,

Yuan

et al. 2024

IEEE Access

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Prediction of Total Electron Content (TEC) in the ionosphere is vital to improve the accuracy of satellite positioning, navigation and remote sensing systems. Most existing TEC prediction methods ignored the local variation patterns between various positions within the TEC sequence, resulting in limited prediction accuracy. To address this issue, this paper combined attention techniques, convolutional neural networks (CNN) and bidirectional long short-term memory networks (BiLSTM) to propose the Att-CNN-BiLSTM model. In the proposed model, CNN is used to extract positional features, BiLSTM is used to extract bidirectional temporal features, and attention technique is used to adaptively weight the features. This paper selected six locations in China, each with a six-year TEC sequence, including three years of high solar activity and three years of low solar activity. The paper first conducted ablation experiments, and the results showed that adding CNN and Attention can effectively improve prediction performance. Then, the proposed model was compared with LSTM and GRU. The experimental results show that compared with LSTM and GRU, the average RMSE of Att-CNN-BiLSTM in six regions decreased by 10.28% and 16.92% in high solar activity years, and by 11.82% and 8.92% in low solar activity years, respectively. The paper also conducted comparative experiments within one week of the magnetic storms, and the results showed that during the magnetic storm period, the RMSE of the proposed model decreased by 35.50% and 37.35% compared to LSTM and GRU. The R 2 s of the proposed model are also higher than those of the comparison models in all cases.

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Section: Discussionmentioning

confidence: 99%

TEC Prediction Based on Att-CNN-BiLSTM

Liu,

Wang,

Yuan

et al. 2024

IEEE Access

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“…This adaptivity makes it highly responsive to variations in the signal, facilitating very effective analysis of nonlinear and nonstationary signals. However, as an adaptive analysis method, its performance depends heavily on two factors: the stopping criterion for the sifting process [59] and the choice of the interpolation method for envelope estimation [60].…”

Section: B Transform-based Methodsmentioning

confidence: 99%

“…However, the performance of the HHT is tied to the choice of AMD method, which is fundamental to the accurate decomposition of the signal into its IMFs. Furthermore, the employed stopping criterion for the sifting process [59] and the used interpolation method for envelope estimation [60], have direct impacts on the effectiveness of the HHT. A proper stopping criterion ensures that the decomposition process neither overfits nor underfits the signal, thereby preserving the signal's essential characteristics without introducing artifacts.…”

Section: ) Empirical Mode Decomposition (Emd)mentioning

confidence: 99%

On the Intersection of Signal Processing and Machine Learning: A Use Case-Driven Analysis Approach

Aburakhia,

Shami,

Karagiannidis

2024

Preprint

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Recent advancements in sensing, measurement, and computing technologies have significantly expanded the potential for signal-based applications, leveraging the synergy between signal processing and Machine Learning (ML) to improve both performance and reliability. This fusion represents a critical point in the evolution of signal-based systems, highlighting the need to bridge the existing knowledge gap between these two interdisciplinary fields. Despite many attempts in the existing literature to bridge this gap, most are limited to specific applications and focus mainly on feature extraction, often assuming extensive prior knowledge in signal processing. This assumption creates a significant obstacle for a wide range of readers. To address these challenges, this paper takes an integrated article approach. It begins with a detailed tutorial on the fundamentals of signal processing, providing the reader with the necessary background knowledge. Following this, it explores the key stages of a standard signal processing-based ML pipeline, offering an in-depth review of feature extraction techniques, their inherent challenges, and solutions. Differing from existing literature, this work offers an application-independent review and introduces a novel classification taxonomy for feature extraction techniques. Furthermore, it aims at linking theoretical concepts with practical applications, and demonstrates this through two specific use cases: a spectral-based method for condition monitoring of rolling bearings and a wavelet energy analysis for epilepsy detection using EEG signals. In addition to theoretical contributions, this work promotes a collaborative research culture by providing a public repository of relevant Python and MATLAB signal processing codes. This effort is intended to support collaborative research efforts and ensure the reproducibility of the results presented.

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“…To evaluate the reliability and dependability of the proposed model, the root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) functions, and Pearson correlations (CORR) are used as the indicators [43][44][45].…”

Section: Methodsmentioning

confidence: 99%

Hybrid Time‐Series Prediction Method Based on Entropy Fusion Feature

Zhang,

Yang,

Feng

2023

International Journal of Intelligent Systems

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High-precision time sequence forecasting is a complicated cyber-physical system (CPS) task. Due to the diversity of data scales and types, the classic time-series prediction model meets the challenge to deliver accurate prediction results for many forms of time-series data. This work proposes a hybrid model with long short-term memory (LSTM) and embedded empirical mode decomposition (EEMD) based on the entropy fusion feature. First, we apply EEMD in entropy fusion feature long short-term memory (ELSTM) to lessen pattern confusion and edge effects in traditional empirical mode decomposition (EMD). The sequence is then divided into intrinsic mode functions (IMF) by using EEMD. Then, feature vectors are constructed between IMFs and their respective information entropy for feature merging. LSTM is used to build a full connection network for each entropy fusion feature IMF subsequence for prediction and each type of IMF subsequence as the feature dimension to obtain its prediction results. Finally, the output results of all IMF subsequences are reconstructed to obtain the final prediction result. Compared with the LSTM method, the performance of the proposed method has been improved 64.33% on the evaluation metric MAPE. The proposed model has also delivered the best prediction outcomes across four different time-series datasets. The experimental results conclusively show that the proposed method outperforms other models compared.

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A New Approach to Empirical Mode Decomposition Based on Akima Spline Interpolation Technique

Cited by 8 publications

References 35 publications

TEC Prediction Based on Att-CNN-BiLSTM

TEC Prediction Based on Att-CNN-BiLSTM

On the Intersection of Signal Processing and Machine Learning: A Use Case-Driven Analysis Approach

Hybrid Time‐Series Prediction Method Based on Entropy Fusion Feature

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