Chaotic forecasting method based on Wavelet Transform

2006 ◽  
Vol 10 (1) ◽  
pp. 49-56
Author(s):  
Yoshiyuki Matsumoto ◽  
Junzo Watada
Keyword(s):  
Wavelet Transform ◽  
Forecasting Method

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.

Electricity Peak Load Demand using De-noising Wavelet Transform integrated with Neural Network Methods

2016 ◽  
Vol 6 (1) ◽  
pp. 12
Author(s):  
Pituk Bunnoon
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Power Systems ◽  
Peak Load ◽  
Electric Power System ◽  
Load Demand ◽  
The Neural Network ◽  
Forecasting Method ◽  
Neural Network Algorithm ◽  
The One

One of most important elements in electric power system planning is load forecasts. So, in this paper proposes the load demand forecasts using de-noising wavelet transform (DNWT) integrated with neural network (NN) methods. This research, the case study uses peak load demand of Thailand (Electricity Generating Authority of Thailand: EGAT). The data of demand will be analyzed with many influencing variables for selecting and classifying factors. In the research, the de-noising wavelet transform uses for decomposing the peak load signal into 2 components these are detail and trend components. The forecasting method using the neural network algorithm is used. The work results are shown a good performance of the model proposed. The result may be taken to the one of decision in the power systems operation.

scholarly journals Electricity Peak Load Demand using De-noising Wavelet Transform integrated with Neural Network Methods

2016 ◽  
Vol 6 (1) ◽  
pp. 12
Author(s):  
Pituk Bunnoon
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Power Systems ◽  
Peak Load ◽  
Electric Power System ◽  
Load Demand ◽  
The Neural Network ◽  
Forecasting Method ◽  
Neural Network Algorithm ◽  
The One

One of most important elements in electric power system planning is load forecasts. So, in this paper proposes the load demand forecasts using de-noising wavelet transform (DNWT) integrated with neural network (NN) methods. This research, the case study uses peak load demand of Thailand (Electricity Generating Authority of Thailand: EGAT). The data of demand will be analyzed with many influencing variables for selecting and classifying factors. In the research, the de-noising wavelet transform uses for decomposing the peak load signal into 2 components these are detail and trend components. The forecasting method using the neural network algorithm is used. The work results are shown a good performance of the model proposed. The result may be taken to the one of decision in the power systems operation.

scholarly journals A hybrid hourly natural gas demand forecasting method based on the integration of wavelet transform and enhanced Deep-RNN model

Energy ◽  
2019 ◽  
Vol 178 ◽  
pp. 585-597 ◽  
Author(s):  
Huai Su ◽  
Enrico Zio ◽  
Jinjun Zhang ◽  
Mingjing Xu ◽  
Xueyi Li ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Natural Gas ◽  
Demand Forecasting ◽  
Forecasting Method ◽  
Natural Gas Demand

Classification of Acceleration Waveforms during Walking by Wavelet Transform

1997 ◽  
Vol 36 (04/05) ◽  
pp. 356-359 ◽  
Author(s):  
M. Sekine ◽  
M. Ogawa ◽  
T. Togawa ◽  
Y. Fukui ◽  
T. Tamura
Keyword(s):  
Wavelet Transform ◽  
Wavelet Analysis ◽  
Center Of Gravity ◽  
Dynamic Responses ◽  
Sequential Data ◽  
Acceleration Signal ◽  
Continuous Dynamic

Abstract:In this study we have attempted to classify the acceleration signal, while walking both at horizontal level, and upstairs and downstairs, using wavelet analysis. The acceleration signal close to the body’s center of gravity was measured while the subjects walked in a corridor and up and down a stairway. The data for four steps were analyzed and the Daubecies 3 wavelet transform was applied to the sequential data. The variables to be discriminated were the waveforms related to levels -4 and -5. The sum of the square values at each step was compared at levels -4 and -5. Downstairs walking could be discriminated from other types of walking, showing the largest value for level -5. Walking at horizontal level was compared with upstairs walking for level -4. It was possible to discriminate the continuous dynamic responses to walking by the wavelet transform.

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