On the use of wavelets packet decomposition for time series prediction

Ravikumar, K. M.; Tamilselvan, S.

doi:10.12988/ams.2014.43172

Cited by 7 publications

(5 citation statements)

References 6 publications

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“…In addition, the approximation and detail coefficients were calculated for all EEG bands: alpha (

), beta (

), theta (

), gamma (γ), and delta (

). Here, three-level WPD was performed to develop a binary tree (23 = 8) [ 26 ]. The proposed study used the detail coefficients to calculate the feature values.…”

Section: Methodsmentioning

confidence: 99%

Classification of Sleep Apnea Based on Sub-Band Decomposition of EEG Signals

Jayaraj

Mani

2021

Diagnostics

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To classify between normal and sleep apnea subjects based on sub-band decomposition of electroencephalogram (EEG) signals. This study comprised 159 subjects obtained from the ISRUC (Institute of System and Robotics—University of Coimbra), Sleep-EDF (European Data Format), and CAP (Cyclic Alternating Pattern) Sleep database, which consists of normal and sleep apnea subjects. The wavelet packet decomposition method was incorporated to categorize the EEG signals into five frequency bands, namely, alpha, beta, delta, gamma, and theta. Entropy and energy (non-linear) for all bands was calculated and as a result, 10 features were obtained for each EEG signal. The ratio of EEG bands included four parameters, including heart rate, brain perfusion, neural activity, and synchronization. In this study, a support vector machine with kernels and random forest classifiers was used for classification. The performance measures demonstrated that the improved results were obtained from the support vector machine classifier with a kernel polynomial order 2. The accuracy (90%), sensitivity (100%), and specificity (83%) with 14 features were estimated using the data obtained from ISRUC database. The proposed study is feasible and seems to be accurate in classifying the subjects with sleep apnea based on the extracted features from EEG signals using a support vector machine classifier.

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“…In addition, the approximation and detail coefficients were calculated for all EEG bands: alpha (

), beta (

), theta (

), gamma (γ), and delta (

). Here, three-level WPD was performed to develop a binary tree (23 = 8) [ 26 ]. The proposed study used the detail coefficients to calculate the feature values.…”

Section: Methodsmentioning

confidence: 99%

Classification of Sleep Apnea Based on Sub-Band Decomposition of EEG Signals

Jayaraj

Mani

2021

Diagnostics

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“…Wang et al proposed a hybrid model based on the combination of wavelet packet transform (WPT), 29 particle swarm optimisation and the simulated annealing algorithm (PSOSA), phase space reconstruction (PSR) and LSSVM for wind speed forecasting. The experiments indicated that the use of the WPT technique could provide a fine‐grained approach to signal analysis 30 . It can decompose the high‐frequency and low‐frequency parts of complex signals into different frequency bands and adaptively select the corresponding frequency bands to match the signal spectrum according to the signal characteristics and analysis requirements, which is a finer decomposition method than WT.…”

Section: Introductionmentioning

confidence: 99%

“…The experiments indicated that the use of the WPT technique could provide a fine-grained approach to signal analysis. 30 It can decompose the high-frequency and lowfrequency parts of complex signals into different frequency bands and adaptively select the corresponding frequency bands to match the signal spectrum according to the signal characteristics and analysis requirements, which is a finer decomposition method than WT.…”

mentioning

confidence: 99%

An effective wind speed prediction model combining secondary decomposition and regularised extreme learning machine optimised by cuckoo search algorithm

Zhang

Guo

et al. 2022

Wind Energy

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Wind speed prediction has an important impact on the planning, economic operation and safe maintenance of wind power systems. However, the high volatility and intermittency of wind speed make it difficult to predict accurately. To improve the prediction accuracy, we developed a hybrid multistep wind speed prediction model named EWP-CS-RELM. In this model, a secondary decomposition technique of ensemble empirical mode decomposition (EEMD) and wavelet packet transform (WPT) is used, and it is called the EWP decomposition technique. This decomposition technique can achieve an adaptive processing of the data and accurately extract the characteristic components of the signal, avoiding the occurrence of pattern overlap and reducing the mutual interference between components. At the same time, the high and low-frequency parts of the complex signal (component) can be decomposed into different frequency bands, and the corresponding frequency band can be selected adaptively to match the signal spectrum. The subsequence obtained after EWP decomposition is then predicted using a regularised extreme learning machine (RELM) optimised by the cuckoo search (CS) algorithm with strong global search ability to obtain the results. The hybrid prediction model is validated using four seasons of wind speed data from two wind farms in Shandong, China, and compared with seven other prediction models. Simulation results illustrate that the EWP-CS-RELM model outperforms the other seven models with the smallest statistical errors.

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“…Recently, hybrid time series modeling approaches utilizing wavelet transform have been one of the research themes studied actively in the hydrological field [8][9][10][11][12][13][14]. In terms of signal analysis, the wavelet transform is a signal decomposition method which splits an original signal into sub-signals, including detail and approximation (smooth) components.…”

Section: Introductionmentioning

confidence: 99%

River Stage Modeling by Combining Maximal Overlap Discrete Wavelet Transform, Support Vector Machines and Genetic Algorithm

Seo

Choi

2017

Water

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This paper proposes a river stage modeling approach combining maximal overlap discrete wavelet transform (MODWT), support vector machines (SVMs) and genetic algorithm (GA). The MODWT decomposes original river stage time series into sub-time series (detail and approximation components). The SVM computes daily river stage values using the decomposed sub-time series. The GA searches for the optimal hyperparameters of SVM. The performance of MODWT-SVM models is evaluated using efficiency and effectiveness indices; and compared with that of a single model (multilayer perceptron (MLP) and SVM), discrete wavelet transform (DWT)-based models (DWT-MLP and DWT-SVM) and MODWT-MLP models. The conjunction of MODWT, SVM and GA improves the performance of the SVM model and outperforms the single models. The MODWT-based models using the SVM model enhance model performance and accuracy compared to those of using MLP model. Also, hybrid models coupling MODWT, SVM and GA improve model performance and accuracy in daily river stage modeling as compared with those combined with DWT. The MODWT-SVM model using the Coiflet 12 (c12) mother wavelet, MODWT-SVM-c12, produces the best efficiency and effectiveness among all models. Therefore, the conjunction of MODWT, SVM and GA can be an efficient and effective approach for modeling daily river stages.

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On the use of wavelets packet decomposition for time series prediction

Cited by 7 publications

References 6 publications

Classification of Sleep Apnea Based on Sub-Band Decomposition of EEG Signals

Classification of Sleep Apnea Based on Sub-Band Decomposition of EEG Signals

An effective wind speed prediction model combining secondary decomposition and regularised extreme learning machine optimised by cuckoo search algorithm

River Stage Modeling by Combining Maximal Overlap Discrete Wavelet Transform, Support Vector Machines and Genetic Algorithm

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