A Study on Stock Graph Recognition Based on Wavelet Denoising and DTW Algorithm
The “classical pattern” of stock price formation has long been widely used in the determination of future price trends of stocks, and the identification and analysis of classical price patterns have an important guiding role in investors’ decision-making and trading. The wavelet transform is a usefu...
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Online Access: | http://dx.doi.org/10.1155/2021/6641749 |
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doaj-3e4f59fd14d04044bdf13ae2fa9900602021-02-15T12:53:08ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472021-01-01202110.1155/2021/66417496641749A Study on Stock Graph Recognition Based on Wavelet Denoising and DTW AlgorithmJi Wei Luo0Shanxi University of Finance & Economics, No. 696, Wucheng Road, Taiyuan, Shanxi, ChinaThe “classical pattern” of stock price formation has long been widely used in the determination of future price trends of stocks, and the identification and analysis of classical price patterns have an important guiding role in investors’ decision-making and trading. The wavelet transform is a useful tool to remove some of the noise of time series because it has the characteristic of multiresolution. In this study, we propose a method for stock price pattern recognition based on the wavelet transform and dynamic time warp (DTW). A pattern recognition method with similar quantified results is developed to obtain accurate pattern recognition results. That is, using the wavelet transform to smooth the original price graph, and then using the DTW algorithm improved in this study to find the graph with the smallest distance from the target graph under the sliding window method, the identification and analysis of the target graph can be realized. In order to improve the recognition rate of the target graph, we preprocessed the raw price sequence using the moving average convergence and divergence (MACD) algorithm based on the control experiments set up in this study. The pattern recognition method used in this study will identify the price patterns of a certain time window as a whole, thus avoiding the problem of how to objectively select the important points that constitute a price pattern and the mathematical definition of different price patterns in the previous traditional methods.http://dx.doi.org/10.1155/2021/6641749 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Ji Wei Luo |
spellingShingle |
Ji Wei Luo A Study on Stock Graph Recognition Based on Wavelet Denoising and DTW Algorithm Mathematical Problems in Engineering |
author_facet |
Ji Wei Luo |
author_sort |
Ji Wei Luo |
title |
A Study on Stock Graph Recognition Based on Wavelet Denoising and DTW Algorithm |
title_short |
A Study on Stock Graph Recognition Based on Wavelet Denoising and DTW Algorithm |
title_full |
A Study on Stock Graph Recognition Based on Wavelet Denoising and DTW Algorithm |
title_fullStr |
A Study on Stock Graph Recognition Based on Wavelet Denoising and DTW Algorithm |
title_full_unstemmed |
A Study on Stock Graph Recognition Based on Wavelet Denoising and DTW Algorithm |
title_sort |
study on stock graph recognition based on wavelet denoising and dtw algorithm |
publisher |
Hindawi Limited |
series |
Mathematical Problems in Engineering |
issn |
1024-123X 1563-5147 |
publishDate |
2021-01-01 |
description |
The “classical pattern” of stock price formation has long been widely used in the determination of future price trends of stocks, and the identification and analysis of classical price patterns have an important guiding role in investors’ decision-making and trading. The wavelet transform is a useful tool to remove some of the noise of time series because it has the characteristic of multiresolution. In this study, we propose a method for stock price pattern recognition based on the wavelet transform and dynamic time warp (DTW). A pattern recognition method with similar quantified results is developed to obtain accurate pattern recognition results. That is, using the wavelet transform to smooth the original price graph, and then using the DTW algorithm improved in this study to find the graph with the smallest distance from the target graph under the sliding window method, the identification and analysis of the target graph can be realized. In order to improve the recognition rate of the target graph, we preprocessed the raw price sequence using the moving average convergence and divergence (MACD) algorithm based on the control experiments set up in this study. The pattern recognition method used in this study will identify the price patterns of a certain time window as a whole, thus avoiding the problem of how to objectively select the important points that constitute a price pattern and the mathematical definition of different price patterns in the previous traditional methods. |
url |
http://dx.doi.org/10.1155/2021/6641749 |
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