A Novel Hybrid Forecasting Method Using GRNN Combined With Wavelet Transform and a GARCH Model

2015 ◽  
Vol 10 (4) ◽  
pp. 418-426 ◽  
Author(s):  
J. Zhang ◽  
Z. Tan ◽  
C. Li
Keyword(s):  
Wavelet Transform ◽  
Garch Model ◽  
Forecasting Method ◽  
Hybrid Forecasting

scholarly journals A Review of Crude Oil Prices Forecasting using Hybrid Method

2018 ◽  
Vol 11 (3) ◽  
pp. 1114
Author(s):  
Nurull Qurraisya Nadiyya Md-Khair ◽  
Ruhaidah Samsudin
Keyword(s):  
Crude Oil ◽  
Review Paper ◽  
Oil Prices ◽  
Economic Activities ◽  
Data Decomposition ◽  
Forecasting Models ◽  
Forecasting Methods ◽  
Crude Oil Prices ◽  
Forecasting Method ◽  
Hybrid Forecasting

Crude oil is considered as a crucial energy source in modern days. Consequently, the fluctuation of crude oil prices can cause a significant impact on economic activities. Researchers have proposed many hybrid forecasting models on top of single forecasting methods which are utilized to predict crude oil prices movement more accurately. Nevertheless, many limitations still existed in hybrid forecasting models and models that can predict crude oil prices as accurate as possible is required. The motivations of this review paper are to identify and assess the mostly used crude oil prices forecasting methods and to analyse their current limitations. 12 studies that used “decomposition-and-ensemble” framework was selected for review. Wavelet transform is identified as the mostly used data decomposition method while some limitations have been recognized. Future researches should include more studies to further elucidate the limitations in existing forecasting method so that subsequent forecasting methods can be improved.

Time-series-based hybrid mathematical modelling method adapted to forecast automotive and medical waste generation: Case study of Lithuania

2018 ◽  
Vol 36 (5) ◽  
pp. 454-462 ◽  
Author(s):  
Aistė Karpušenkaitė ◽  
Tomas Ruzgas ◽  
Gintaras Denafas
Keyword(s):  
Time Series ◽  
Mathematical Modelling ◽  
Error Rate ◽  
Medical Waste ◽  
Waste Generation ◽  
Prediction Horizon ◽  
Modelling Method ◽  
Forecasting Method ◽  
Hybrid Forecasting

The aim of the study was to create a hybrid forecasting method that could produce higher accuracy forecasts than previously used ‘pure’ time series methods. Mentioned methods were already tested with total automotive waste, hazardous automotive waste, and total medical waste generation, but demonstrated at least a 6% error rate in different cases and efforts were made to decrease it even more. Newly developed hybrid models used a random start generation method to incorporate different time-series advantages and it helped to increase the accuracy of forecasts by 3%–4% in hazardous automotive waste and total medical waste generation cases; the new model did not increase the accuracy of total automotive waste generation forecasts. Developed models’ abilities to forecast short- and mid-term forecasts were tested using prediction horizon.

Prediction Based on a Multiscale Decomposition

2003 ◽  
Vol 01 (02) ◽  
pp. 217-232 ◽  
Author(s):  
Olivier Renaud ◽  
Jean-Luc Starck ◽  
Fionn Murtagh
Keyword(s):  
Time Series ◽  
Wavelet Transform ◽  
Long Range ◽  
Linear Prediction ◽  
Financial Data ◽  
Simulation Studies ◽  
Optimal Prediction ◽  
Short Term ◽  
Forecasting Method ◽  
Sparse Modelling

A wavelet-based forecasting method for time series is introduced. It is based on a multiple resolution decomposition of the signal, using the redundant "à trous" wavelet transform which has the advantage of being shift-invariant. The result is a decomposition of the signal into a range of frequency scales. The prediction is based on a small number of coefficients on each of these scales. In its simplest form it is a linear prediction based on a wavelet transform of the signal. This method uses sparse modelling, but can be based on coefficients that are summaries or characteristics of large parts of the signal. The lower level of the decomposition can capture the long-range dependencies with only a few coefficients, while the higher levels capture the usual short-term dependencies. We show the convergence of the method towards the optimal prediction in the autoregressive case. The method works well, as shown in simulation studies, and studies involving financial data.

scholarly journals A Hybrid Forecasting Method for Wind Power Ramp Based on Orthogonal Test and Support Vector Machine (OT-SVM)

2017 ◽  
Vol 8 (2) ◽  
pp. 451-457 ◽  
Author(s):  
Yongqian Liu ◽  
Ying Sun ◽  
David Infield ◽  
Yu Zhao ◽  
Shuang Han ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Wind Power ◽  
Orthogonal Test ◽  
Support Vector ◽  
Forecasting Method ◽  
Hybrid Forecasting

scholarly journals Machine Intelligent Hybrid Methods Based on Kalman Filter and Wavelet Transform for Short-Term Wind Speed Prediction

Wind ◽  
2022 ◽  
Vol 2 (1) ◽  
pp. 37-50
Author(s):  
Yug Patel ◽  
Dipankar Deb
Keyword(s):  
Kalman Filter ◽  
Wavelet Transform ◽  
Wind Speed ◽  
Hybrid Methods ◽  
Moving Average ◽  
Offshore Wind ◽  
Electricity Grid ◽  
Wind Speed Prediction ◽  
Speed Prediction ◽  
Hybrid Forecasting

Wind power’s increasing penetration into the electricity grid poses several challenges for power system operators, primarily due to variability and unpredictability. Highly accurate wind predictions are needed to address this concern. Therefore, the performance of hybrid forecasting approaches combining autoregressive integrated moving average (ARIMA), machine learning models (SVR, RF), wavelet transform (WT), and Kalman filter (KF) techniques is essential to examine. Comparing the proposed hybrid methods with available state-of-the-art algorithms shows that the proposed approach provides more accurate prediction results. The best model is a hybrid of KF-WT-ML with an average R2 score of 0.99967 and RMSE of 0.03874, followed by ARIMA-WT-ML with an average R2 of 0.99796 and RMSE of 0.05863 over different datasets. Moreover, the KF-WT-ML model evaluated on different terrains, including offshore and hilly regions, reveals that the proposed KF based hybrid provides accurate wind speed forecasts for both onshore and offshore wind data.

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