基于滑动平均与分段线性回归的时间序列相似性

计算机科学 ›› 2018, Vol. 45 ›› Issue (6A): 110-113.

基于滑动平均与分段线性回归的时间序列相似性

冯玉伯^1,2,丁承君¹,高雪¹,朱雪宏¹,刘强¹

河北工业大学机械工程学院天津300130 ¹
泰华宏业天津机器人技术研究院有限责任公司天津300130²

出版日期:2018-06-20 发布日期:2018-08-03
作者简介:冯玉伯(1974-),男,博士,高级工程师,主要研究方向为物联网、机器学习、数据挖掘;丁承君(1973-),男,博士,教授,博士生导师,主要研究方向为移动机器人智能控制、嵌入式计算机系统,E-mail:190532210@qq.com(通信作者);高雪(1991-),女,硕士生,主要研究方向为移动机器人智能控制、嵌入式计算机系统;朱雪宏(1987-),博士,主要研究方向为移动机器人智能控制、嵌入式计算机系统;刘强(1993-),男,硕士生,主要研究方向为移动机器人智能控制、嵌入式计算机系统。
基金资助:
天津市科技支撑计划项目(15ZXHLGX00210,14ZCDZGX00811),天津市科技支撑计划(13ZCZDGX01200),天津市产学研合作项目(14ZCZDSF00025),天津市863成果转化项目(13RCHZGX01116),天津市863成果转化项目(14RCHZGX00862)资助

Time Series Similarity Based on Moving Average and Piecewise Linear Regression

FENG Yu-bo^1,2,DING Cheng-jun¹,GAO Xue¹,ZHU Xue-hong¹,LIU Qiang¹

School of Mechanical Engineering,Hebei University of Technology,Tianjin 300130,China¹
Taihua Hongye Tianjin Robot Technology Research Institute Co.Ltd.,Tianjin 300130,China²

Online:2018-06-20 Published:2018-08-03

摘要/Abstract

摘要： 针对时间序列相似性度量中欧氏距离对异常数据敏感以及DTW距离算法效率低的问题,提出基于滑动平均与分段线性回归的时间序列相似性方法。首先,使用初始可变滑动平均算法以及分段线性回归对原始时间序列进行数据变换,并将分段线性回归的参数(截距与距离)集作为时间序列的特征,以实现时间序列的特征提取和数据降维;然后,利用动态时间弯曲距离进行距离计算。该方法在时间序列相似性上与DTW算法的性能相近,但是在算法效率上几乎提高了96%。实验结果验证了该方法的有效性与准确性。

关键词: 动态时间弯曲距离, 滑动平均, 时间序列, 线性回归

Abstract: Aiming at the problems that the Euclidean distance is sensitive to the anomaly data and the efficiency of the DTW distance algorithm is low,a time series similarity method based on the moving average and the piecewise linear regression was proposed.Firstly,the original variable-averaging algorithm and the piecewise linear regression are used to transform the original time series.The parameters of the piecewise linear regression (intercept and distance) are taken as the characteristics of the time series so that the feature extraction of the time series is realized,and the data is dimensioned.Then it calculated distance using the dynamic time bending distance.The performance of the method is similar tothat of DTW algorithm,but the proposed method is almost 96% higher in algorithm efficiency.The experimental results verify the effectiveness and accuracy of the method.

Key words: Dynamic time warping(DTW), Linear regression, Moving average, Time series

中图分类号:

TP311.13

冯玉伯,丁承君,高雪,朱雪宏,刘强. 基于滑动平均与分段线性回归的时间序列相似性[J]. 计算机科学, 2018, 45(6A): 110-113. https://doi.org/

FENG Yu-bo,DING Cheng-jun,GAO Xue,ZHU Xue-hong,LIU Qiang. Time Series Similarity Based on Moving Average and Piecewise Linear Regression[J]. Computer Science, 2018, 45(6A): 110-113. https://doi.org/

参考文献

[1]李海林,杨丽彬.时间序列数据降维和特征表示方法.控制与决策,2013,28(11):1718-1722.
[2]Al-NAYMAT G,TAHERI J.Effects of dimensionality reduction techniques on time series similarity measurements∥IEEE/ACS International Conference on Computer Systems and Applications.Piscataway:IEEE,2008:188-195.
[3]KEOGH E,RATANAMAHATANA C A.Exact indexing of dynamic time warping[J].Knowledge and Information Systems,2005,7(3):358-386.
[4]KEOGH E,PAZZANI M.Derivative dynamic time warping[C]∥ The First SIAM International Conference on Data Mining.Washington:IEEE,2001:1-11.
[5]王达,荣冈.时间序列的模式距离[J].浙江大学学报工学版,2004,38(7):795-798.
[6]DONG X L,GU C K,WANG Z O.Study on Time Series Similarity Measurement Based on Morphology [J].Journal of Electronics & Information Technology,2007,29(5):1228-1231.
[7] 张鹏,李学仁,张建业,等.时间序列的夹角距离及相似性搜索.模式识别与人工智能,2008,21(6):763-767.
[8]陆薛妹,胡轶,方建安.基于分段极值DTW距离的时间序列相似性度量.微计算机信息,2007,23(27):204-206.
[9]李海林,郭崇慧,杨丽彬.基于分段聚合时间弯曲距离的时间序列挖掘.山东大学学报(工学版),2011,41(5):57-62.
[10]朱天,白似雪.基于模式距离度量的时间序列相似性搜索.微计算机信息,2007,23(30):216-217.
[11]RABINER L,JUANG B H.Fundamentals of Speech Recognition.Tsinghua University Press,1993,1(1):353-356.
[12]VULLINGS H J L M,VERHAEGEN M H G,VERBRUGGEN H B.ECG segmentation using time-warping∥Advances in Intelligent Data Analysis Reasoning about Data.Springer Berlin Heidelberg,1997:275-285.
[13]BERNDT D J,CLIFFORD J.Using dynamic time warping to find patterns in time series∥KDD workshop.Seattle:AAAI Press,1994:359-370.
[14]BERNDT D J,CLIFFORD J.Finding patterns in time series:a dynamic programming approach∥Advances in Knowledge Discovery & Data Mining.Washington:American Association for Artificial Intelligence,1996:229-248.

相关文章 15

[1]	吕由, 吴文渊. 隐私保护线性回归方案与应用 Privacy-preserving Linear Regression Scheme and Its Application 计算机科学, 2022, 49(9): 318-325. https://doi.org/10.11896/jsjkx.220300190
[2]	高振卓, 王志海, 刘海洋. 嵌入典型时间序列特征的随机Shapelet森林算法 Random Shapelet Forest Algorithm Embedded with Canonical Time Series Features 计算机科学, 2022, 49(7): 40-49. https://doi.org/10.11896/jsjkx.210700226
[3]	刘宝宝, 杨菁菁, 陶露, 王贺应. 基于DE-LSTM模型的教育统计数据预测研究 Study on Prediction of Educational Statistical Data Based on DE-LSTM Model 计算机科学, 2022, 49(6A): 261-266. https://doi.org/10.11896/jsjkx.220300120
[4]	高堰泸, 徐圆, 朱群雄. 基于A-DLSTM夹层网络结构的电能消耗预测方法 Predicting Electric Energy Consumption Using Sandwich Structure of Attention in Double -LSTM 计算机科学, 2022, 49(3): 269-275. https://doi.org/10.11896/jsjkx.210100006
[5]	陈长伟, 周晓峰. 快速局部协同表示分类器及其在人脸识别中的应用 Fast Local Collaborative Representation Based Classifier and Its Applications in Face Recognition 计算机科学, 2021, 48(9): 208-215. https://doi.org/10.11896/jsjkx.200800155
[6]	张争万, 吴迪, 张春炯. 基于多通道稀疏LSTM的蜂窝流量预测研究 Study of Cellular Traffic Prediction Based on Multi-channel Sparse LSTM 计算机科学, 2021, 48(6): 296-300. https://doi.org/10.11896/jsjkx.210400134
[7]	黄铭, 孙林夫, 任春华, 吴奇石. 改进KNN的时间序列分析方法 Improved KNN Time Series Analysis Method 计算机科学, 2021, 48(6): 71-78. https://doi.org/10.11896/jsjkx.200500044
[8]	李艾玲, 张凤荔, 高强, 王瑞锦. 基于自适应时间戳与多尺度特征提取的轨迹下一足迹预测模型 Trajectory Next Footprint Prediction Model Based on Adaptive Timestamp and Multi-scale Feature Extraction 计算机科学, 2021, 48(11A): 191-197. https://doi.org/10.11896/jsjkx.201200015
[9]	王晓迪, 刘鑫, 于晓. 用于多元时间序列预测的自适应频域模型 Adaptive Frequency Domain Model for Multivariate Time Series Forecasting 计算机科学, 2021, 48(11A): 204-210. https://doi.org/10.11896/jsjkx.210500129
[10]	杨章静, 王文博, 黄璞, 张凡龙, 王昕. 基于局部加权表示的线性回归分类器及人脸识别 Local Weighted Representation Based Linear Regression Classifier and Face Recognition 计算机科学, 2021, 48(11A): 351-359. https://doi.org/10.11896/jsjkx.210100173
[11]	王新平, 夏春明, 颜建军. 基于肌音信号图像化和卷积神经网络的手语识别研究 Sign Language Recognition Based on Image-interpreted Mechanomyography and Convolution Neural Network 计算机科学, 2021, 48(11): 242-249. https://doi.org/10.11896/jsjkx.201000019
[12]	邵政毅, 陈秀宏. 基于样本特征核矩阵的稀疏双线性回归 Sample Feature Kernel Matrix-based Sparse Bilinear Regression 计算机科学, 2021, 48(10): 185-190. https://doi.org/10.11896/jsjkx.200800219
[13]	冯安琪, 钱丽萍, 欧阳金源, 吴远. 车联网络通过两级量化自适应卡尔曼滤波实现车辆状态预测 Vehicular Networking Enabled Vehicle State Prediction with Two-level Quantized AdaptiveKalman Filtering 计算机科学, 2020, 47(5): 230-235. https://doi.org/10.11896/jsjkx.190300155
[14]	闫祥祥. 使用ARIMA模型预测公园绿地面积 Using ARIMA Model to Predict Green Area of Park 计算机科学, 2020, 47(11A): 531-534. https://doi.org/10.11896/jsjkx.200300099
[15]	丁武, 马媛, 杜诗蕾, 李海辰, 丁公博, 王超. 基于XGBoost算法的多元水文时间序列趋势相似性挖掘 Mining Trend Similarity of Multivariate Hydrological Time Series Based on XGBoost Algorithm 计算机科学, 2020, 47(11A): 459-463. https://doi.org/10.11896/jsjkx.200500128

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed