scholarly journals Two-stage Variational Mode Decomposition and Support Vector Regression for Streamflow Forecasting

2019 ◽  
Author(s):  
Ganggang Zuo ◽  
Jungang Luo ◽  
Ni Wang ◽  
Yani Lian ◽  
Xinxin He
Keyword(s):  
Singular Spectrum Analysis ◽  
Boundary Effects ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Variational Mode Decomposition ◽  
Streamflow Forecasting ◽  
Two Stage ◽  
Signal Component ◽  
Mode Decomposition ◽  
Forecasting Performance

Abstract. Streamflow forecasting is a crucial component in the management and control of water resources. Decomposition-based approaches have particularly demonstrated improved streamflow forecasting performance. However, it is not practical to firstly decompose the entire streamflow into several signal components and then divide the data samples of each component into training and validation sets for signal component prediction. This impracticality is due to the fact that some validation information, that is not available in practical streamflow forecasting, is used in that training process. Unfortunately, firstly dividing the entire streamflow into training and validation sets and then decomposing each set separately lead to undesirable boundary effects and complicated forecasting. Moreover, establishing a model for each signal component is quite laborious and summing the component predictions may lead to error accumulation. In addition, summing the decomposition results may sometimes lead to inaccurate reconstruction of the original streamflow. In order to address these shortcomings of decomposition-based models and improve the forecasting performance in basins lacking meteorological observations (e.g., precipitation and temperature), we propose a two-stage decomposition prediction (TSDP) framework, realize this framework using variational mode decomposition (VMD) and support vector machines (SVR), and refer to this realization as VMD-SVR. In the first stage of the TSDP framework, the entire streamflow data was divided into training and validation sets, each of which was then separately decomposed to avoid the influence of validation information on training. In the second stage, a single model for streamflow prediction was established using a set of mixed shuffled samples. This scheme saves the modelling time and reduces the influence of the boundary effects. We demonstrate experimentally the effectiveness, efficiency and reliability of the TSDP framework and its VMD-SVR realization in terms of the boundary effect reduction, decomposition performance, prediction outcomes, time consumption, overfitting, and forecasting capability for long leading times. Specifically, five comparative experiments were conducted based on the ensemble empirical mode decomposition (EEMD), singular spectrum analysis (SSA), discrete wavelet transform (DWT) and SVR. The experimental results on monthly runoff collected from three stations at the Wei River show the superiority of the TSDP framework compared to benchmark models.

scholarly journals Two-stage variational mode decomposition and support vector regression for streamflow forecasting

2020 ◽  
Vol 24 (11) ◽  
pp. 5491-5518
Author(s):  
Ganggang Zuo ◽  
Jungang Luo ◽  
Ni Wang ◽  
Yani Lian ◽  
Xinxin He
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Boundary Effects ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Variational Mode Decomposition ◽  
Streamflow Forecasting ◽  
Calibration And Validation ◽  
Mode Decomposition ◽  
Forecasting Performance

Abstract. Streamflow forecasting is a crucial component in the management and control of water resources. Decomposition-based approaches have particularly demonstrated improved forecasting performance. However, direct decomposition of entire streamflow data with calibration and validation subsets is not practical for signal component prediction. This impracticality is due to the fact that the calibration process uses some validation information that is not available in practical streamflow forecasting. Unfortunately, independent decomposition of calibration and validation sets leads to undesirable boundary effects and less accurate forecasting. To alleviate such boundary effects and improve the forecasting performance in basins lacking meteorological observations, we propose a two-stage decomposition prediction (TSDP) framework. We realize this framework using variational mode decomposition (VMD) and support vector regression (SVR) and refer to this realization as VMD-SVR. We demonstrate experimentally the effectiveness, efficiency and accuracy of the TSDP framework and its VMD-SVR realization in terms of the boundary effect reduction, computational cost, and overfitting, in addition to decomposition and forecasting outcomes for different lead times. Specifically, four comparative experiments were conducted based on the ensemble empirical mode decomposition (EEMD), singular spectrum analysis (SSA), discrete wavelet transform (DWT), boundary-corrected maximal overlap discrete wavelet transform (BCMODWT), autoregressive integrated moving average (ARIMA), SVR, backpropagation neural network (BPNN) and long short-term memory (LSTM). The TSDP framework was also compared with the wavelet data-driven forecasting framework (WDDFF). Results of experiments on monthly runoff data collected from three stations at the Wei River show the superiority of the VMD-SVR model compared to benchmark models.

scholarly journals Two-Stage Hybrid Machine Learning Model for High-Frequency Intraday Bitcoin Price Prediction Based on Technical Indicators, Variational Mode Decomposition, and Support Vector Regression

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Samuel Asante Gyamerah
Keyword(s):  
Support Vector Regression ◽  
Technical Analysis ◽  
Support Vector ◽  
Variational Mode Decomposition ◽  
Two Stage ◽  
Technical Indicators ◽  
Price Prediction ◽  
Price Series ◽  
Mode Decomposition ◽  
Statistical Trends

Due to the inherent chaotic and fractal dynamics in the price series of Bitcoin, this paper proposes a two-stage Bitcoin price prediction model by combining the advantage of variational mode decomposition (VMD) and technical analysis. VMD eliminates the noise signals and stochastic volatility in the price data by decomposing the data into variational mode functions, while technical analysis uses statistical trends obtained from past trading activity and price changes to construct technical indicators. The support vector regression (SVR) accepts input from a hybrid of technical indicators (TI) and reconstructed variational mode functions (rVMF). The model is trained, validated, and tested in a period characterized by unprecedented economic turmoil due to the COVID-19 pandemic, allowing the evaluation of the model in the presence of the pandemic. The constructed hybrid model outperforms the single SVR model that uses only TI and rVMF as features. The ability to predict a minute intraday Bitcoin price has a huge propensity to reduce investors’ exposure to risk and provides better assurances of annualized returns.

scholarly journals Ensemble streamflow forecasting based on variational mode decomposition and long short term memory

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Xiaomei Sun ◽  
Haiou Zhang ◽  
Jian Wang ◽  
Chendi Shi ◽  
Dongwen Hua ◽  
...  
Keyword(s):  
Short Term Memory ◽  
Support Vector ◽  
Variational Mode Decomposition ◽  
Streamflow Forecasting ◽  
Short Term ◽  
Term Memory ◽  
Historical Changes ◽  
Daily Streamflow ◽  
Mode Decomposition ◽  
Long Short Term Memory

AbstractReliable and accurate streamflow forecasting plays a vital role in the optimal management of water resources. To improve the stability and accuracy of streamflow forecasting, a hybrid decomposition-ensemble model named VMD-LSTM-GBRT, which is sensitive to sampling, noise and long historical changes of streamflow, was established. The variational mode decomposition (VMD) algorithm was first applied to extract features, which were then learned by several long short-term memory (LSTM) networks. Simultaneously, an ensemble tree, a gradient boosting tree for regression (GBRT), was trained to model the relationships between the extracted features and the original streamflow. The outputs of these LSTMs were finally reconstructed by the GBRT model to obtain the forecasting streamflow results. A historical daily streamflow series (from 1/1/1997 to 31/12/2014) for Yangxian station, Han River, China, was investigated by the proposed model. VMD-LSTM-GBRT was compared with respect to three aspects: (1) feature extraction algorithm; ensemble empirical mode decomposition (EEMD) was used. (2) Feature learning techniques; deep neural networks (DNNs) and support vector machines for regression (SVRs) were exploited. (3) Ensemble strategy; the summation strategy was used. The results indicate that the VMD-LSTM-GBRT model overwhelms all other peer models in terms of the root mean square error (RMSE = 36.3692), determination coefficient (R2 = 0.9890), mean absolute error (MAE = 9.5246) and peak percentage threshold statistics (PPTS(5) = 0.0391%). The addressed approach based on the memory of long historical changes with deep feature representations had good stability and high prediction precision.

scholarly journals Hybrid Empirical Mode Decomposition with Support Vector Regression Model for Short Term Load Forecasting

Energies ◽  
2019 ◽  
Vol 12 (6) ◽  
pp. 1093 ◽  
Author(s):  
Wei-Chiang Hong ◽  
Guo-Feng Fan
Keyword(s):  
Support Vector Regression ◽  
Load Forecasting ◽  
Support Vector ◽  
Electric Load ◽  
Short Term ◽  
Middle Term ◽  
Mode Decomposition ◽  
Random Term ◽  
Forecasting Performance ◽  
Short Term Load Forecasting

For operational management of power plants, it is desirable to possess more precise short-term load forecasting results to guarantee the power supply and load dispatch. The empirical mode decomposition (EMD) method and the particle swarm optimization (PSO) algorithm have been successfully hybridized with the support vector regression (SVR) to produce satisfactory forecasting performance in previous studies. Decomposed intrinsic mode functions (IMFs), could be further defined as three items: item A contains the random term and the middle term; item B contains the middle term and the trend (residual) term, and item C contains the middle terms only, where the random term represents the high-frequency part of the electric load data, the middle term represents the multiple-frequency part, and the trend term represents the low-frequency part. These three items would be modeled separately by the SVR-PSO model, and the final forecasting results could be calculated as A+B-C (the defined item D). Consequently, this paper proposes a novel electric load forecasting model, namely H-EMD-SVR-PSO model, by hybridizing these three defined items to improve the forecasting accuracy. Based on electric load data from the Australian electricity market, the experimental results demonstrate that the proposed H-EMD-SVR-PSO model receives more satisfied forecasting performance than other compared models.

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