The Experts below are selected from a list of 141585 Experts worldwide ranked by ideXlab platform
Xiangxu Meng - One of the best experts on this subject based on the ideXlab platform.
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a piecewise Linear Representation method based on importance data points for time series data
Computer Supported Cooperative Work in Design, 2016Co-Authors: Shijun Liu, Chenglei Yang, Li Pan, Xiangxu MengAbstract:With the development of intelligent manufacturing technology, it can be foreseen that time series data generated by smart devices will raise to an unprecedented level. For time series with high amount, high dimension and renewal speed characteristics, resulting in difficult data mining and presentation on the original time series data. This paper presented a piecewise Linear Representation based on importance data points for time series data, which called PLR_IDP for short. The method finds importance data points by calculating the fitting error of single point and piecewise, and then represents time series approximately by Linear composed of the importance data points. Results from theoretical analysis and experiments show that PLR_IDP reduces the dimensionality, holds the main characteristic with small fitting error of segments and single points.
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CSCWD - A piecewise Linear Representation method based on importance data points for time series data
2016 IEEE 20th International Conference on Computer Supported Cooperative Work in Design (CSCWD), 2016Co-Authors: Shijun Liu, Chenglei Yang, Li Pan, Xiangxu MengAbstract:With the development of intelligent manufacturing technology, it can be foreseen that time series data generated by smart devices will raise to an unprecedented level. For time series with high amount, high dimension and renewal speed characteristics, resulting in difficult data mining and presentation on the original time series data. This paper presented a piecewise Linear Representation based on importance data points for time series data, which called PLR_IDP for short. The method finds importance data points by calculating the fitting error of single point and piecewise, and then represents time series approximately by Linear composed of the importance data points. Results from theoretical analysis and experiments show that PLR_IDP reduces the dimensionality, holds the main characteristic with small fitting error of segments and single points.
Minghsuan Yang - One of the best experts on this subject based on the ideXlab platform.
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spatially variant Linear Representation models for joint filtering
Computer Vision and Pattern Recognition, 2019Co-Authors: Jinshan Pan, Jiangxin Dong, Jimmy Ren, Liang Lin, Jinhui Tang, Minghsuan YangAbstract:Joint filtering mainly uses an additional guidance image as a prior and transfers its structures to the target image in the filtering process. Different from existing algorithms that rely on locally Linear models or hand-designed objective functions to extract the structural information from the guidance image, we propose a new joint filter based on a spatially variant Linear Representation model (SVLRM), where the target image is Linearly represented by the guidance image. However, the SVLRM leads to a highly ill-posed problem. To estimate the Linear Representation coefficients, we develop an effective algorithm based on a deep convolutional neural network (CNN). The proposed deep CNN (constrained by the SVLRM) is able to estimate the spatially variant Linear Representation coefficients which are able to model the structural information of both the guidance and input images. We show that the proposed algorithm can be effectively applied to a variety of applications, including depth/RGB image upsampling and restoration, flash/no-flash image deblurring, natural image denoising, scale-aware filtering, etc. Extensive experimental results demonstrate that the proposed algorithm performs favorably against state-of-the-art methods that have been specially designed for each task.
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CVPR - Spatially Variant Linear Representation Models for Joint Filtering
2019 IEEE CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019Co-Authors: Jinshan Pan, Jiangxin Dong, Jimmy Ren, Liang Lin, Jinhui Tang, Minghsuan YangAbstract:Joint filtering mainly uses an additional guidance image as a prior and transfers its structures to the target image in the filtering process. Different from existing algorithms that rely on locally Linear models or hand-designed objective functions to extract the structural information from the guidance image, we propose a new joint filter based on a spatially variant Linear Representation model (SVLRM), where the target image is Linearly represented by the guidance image. However, the SVLRM leads to a highly ill-posed problem. To estimate the Linear Representation coefficients, we develop an effective algorithm based on a deep convolutional neural network (CNN). The proposed deep CNN (constrained by the SVLRM) is able to estimate the spatially variant Linear Representation coefficients which are able to model the structural information of both the guidance and input images. We show that the proposed algorithm can be effectively applied to a variety of applications, including depth/RGB image upsampling and restoration, flash/no-flash image deblurring, natural image denoising, scale-aware filtering, etc. Extensive experimental results demonstrate that the proposed algorithm performs favorably against state-of-the-art methods that have been specially designed for each task.
Shijun Liu - One of the best experts on this subject based on the ideXlab platform.
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a piecewise Linear Representation method based on importance data points for time series data
Computer Supported Cooperative Work in Design, 2016Co-Authors: Shijun Liu, Chenglei Yang, Li Pan, Xiangxu MengAbstract:With the development of intelligent manufacturing technology, it can be foreseen that time series data generated by smart devices will raise to an unprecedented level. For time series with high amount, high dimension and renewal speed characteristics, resulting in difficult data mining and presentation on the original time series data. This paper presented a piecewise Linear Representation based on importance data points for time series data, which called PLR_IDP for short. The method finds importance data points by calculating the fitting error of single point and piecewise, and then represents time series approximately by Linear composed of the importance data points. Results from theoretical analysis and experiments show that PLR_IDP reduces the dimensionality, holds the main characteristic with small fitting error of segments and single points.
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CSCWD - A piecewise Linear Representation method based on importance data points for time series data
2016 IEEE 20th International Conference on Computer Supported Cooperative Work in Design (CSCWD), 2016Co-Authors: Shijun Liu, Chenglei Yang, Li Pan, Xiangxu MengAbstract:With the development of intelligent manufacturing technology, it can be foreseen that time series data generated by smart devices will raise to an unprecedented level. For time series with high amount, high dimension and renewal speed characteristics, resulting in difficult data mining and presentation on the original time series data. This paper presented a piecewise Linear Representation based on importance data points for time series data, which called PLR_IDP for short. The method finds importance data points by calculating the fitting error of single point and piecewise, and then represents time series approximately by Linear composed of the importance data points. Results from theoretical analysis and experiments show that PLR_IDP reduces the dimensionality, holds the main characteristic with small fitting error of segments and single points.
Xi Chen - One of the best experts on this subject based on the ideXlab platform.
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Integrating piecewise Linear Representation and weighted support vector machine for stock trading signal prediction
Applied Soft Computing, 2013Co-Authors: Linkai Luo, Xi ChenAbstract:Piecewise Linear Representation (PLR) and back-propagation artificial neural network (BPN) have been integrated for the stock trading signal prediction recently (PLR-BPN). However, there are some disadvantages in avoiding over-fitting, trapping in local minimum and choosing the threshold of the trading decision. Since support vector machine (SVM) has a good way to avoid over-fitting and trapping in local minimum, we integrate PLR and weighted SVM (WSVM) to forecast the stock trading signals (PLR-WSVM). The new characteristics of PLR-WSVM are as follows: (1) the turning points obtained from PLR are set by different weights according to the change rate of the closing price between the current turning point and the next one, in which the weight reflects the relative importance of each turning point; (2) the prediction of stock trading signal is formulated as a weighted four-class classification problem, in which it does not need to determine the threshold of trading decision; (3) WSVM is used to model the relationship between the trading signal and the input variables, which improves the generalization performance of prediction model; (4) the history dataset is divided into some overlapping training-testing sets rather than training-validation-testing, which not only makes use of data fully but also reduces the time variability of data; and (5) some new technical indicators representing investors' sentiment are added to the input variables, which improves the prediction performance. The comparative experiments among PLR-WSVM, PLR-BPN and buy-and-hold strategy (BHS) on 20 shares from Shanghai Stock Exchange in China show that the prediction accuracy and profitability of PLR-WSVM are all the best, which indicates PLR-WSVM is effective and can be used in the stock trading signal prediction.
Pei-chann Chang - One of the best experts on this subject based on the ideXlab platform.
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CSIE (5) - Evolving Neural Network with Dynamic Time Warping and Piecewise Linear Representation System for Stock Trading Decision Making
2009 WRI World Congress on Computer Science and Information Engineering, 2009Co-Authors: Pei-chann Chang, Chin-yuan Fan, Chen-hao Liu, Yen-wen Wang, Jyun-jie LinAbstract:Stock turning points detection is a very interesting subject arising in numerous financial and economic planning problems. In this paper, Evolving Neural Network model with Dynamic Time warping Piecewise Linear Representation system for stock turning points detection is presented. The piecewise Linear Representation method is able to generate numerous stocks turning points from the historic data base, then Evolving Neural Network model will be applied to train the pattern and retrieve similar stock price patterns from historic data for training the system. These turning points represent short-term trading signals for selling or buying stocks from the market. it is applied to forecast the future turning points from the set of test data. Experimental results demonstrate that the hybrid system integrating DPLR and evolving neural networks can make a significant and constant amount of profit when compared with other approaches using stock data.
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Integrating a Piecewise Linear Representation Method and a Neural Network Model for Stock Trading Points Prediction
IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews), 2009Co-Authors: Pei-chann Chang, Chin-yuan Fan, Chen-hao LiuAbstract:Recently, the piecewise Linear Representation (PLR) method has been applied to the stock market for pattern matching. As such, similar patterns can be retrieved from historical data and future prices of the stock can be predicted according to the patterns retrieved. In this paper, a different approach is taken by applying PLR to decompose historical data into different segments. As a result, temporary turning points (trough or peak) of the historical stock data can be detected and inputted to the backpropagation neural network (BPN) for supervised training of the model. After this, a new set of test data can trigger the model when a buy or sell point is detected by BPN. An intelligent PLR (IPLR) model is further developed by integrating the genetic algorithm with the PLR to iteratively improve the threshold value of the PLR. Thus, it further increases the profitability of the model. The proposed system is tested on three different types of stocks, i.e., uptrend, steady, and downtrend. The experimental results show that the IPLR approach can make significant amounts of profit on stocks with different variations. In conclusion, the proposed system is very effective and encouraging in that it predicts the future trading points of a specific stock.
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ICNC (2) - Integrating a Piecewise Linear Representation Method with Dynamic Time Warping System for Stock Trading Decision Making
2008 Fourth International Conference on Natural Computation, 2008Co-Authors: Pei-chann Chang, Chin-yuan Fan, Jun-lin Lin, Jyun-jie LinAbstract:Stock turning points detection is a very interesting subject arising in numerous financial and economic planning problems. In this paper, a piecewise Linear Representation method with dynamics time warping system for stock turning points detection is presented. The piecewise Linear Representation method is able to generate numerous stocks turning points from the historic data base, then the dynamic time warping system will be applied to retrieve similar stock price patterns from historic data for training the system. These turning points represent short-term trading signals for selling or buying stocks from the market. A Back-Propagation neural network (B.P.N) is further applied to learn the connection weights from these historic turning points and afterwards it is applied to forecast the future turning points from the set of test data. Experimental results demonstrate that the system integrating PLR and neural networks can make a significant amount of profit when compared with other approaches using stock data.
