计算机科学 ›› 2019, Vol. 46 ›› Issue (1): 21-28.doi: 10.11896/j.issn.1002-137X.2019.01.004

• 综述 • 上一篇    下一篇


杨海民1, 潘志松2, 白玮2   

  1. (陆军工程大学研究生院 南京210007)1
    (陆军工程大学指挥控制工程学院 南京210007)2
  • 收稿日期:2018-02-04 出版日期:2019-01-15 发布日期:2019-02-25
  • 作者简介:杨海民(1990-),男,博士生,主要研究方向为机器学习、时间序列预测,E-mail:haiminyang_nj@126.com;潘志松(1973-),男,教授,博士生导师,主要研究方向为机器学习、网络空间安全,E-mail:haiminyang_nj@126.com(通信作者);白 玮(1983-),男,讲师,主要研究方向为网络空间安全。
  • 基金资助:

Review of Time Series Prediction Methods

YANG Hai-min1, PAN Zhi-song2, BAI Wei2   

  1. (School of Graduate,Army Engineering University of PLA,Nanjing 210007,China)1
    (College of Command and Control Engineering,Army Engineering University of PLA,Nanjing 210007,China)2
  • Received:2018-02-04 Online:2019-01-15 Published:2019-02-25

摘要: 时间序列是按照时间排序的一组随机变量,它通常是在相等间隔的时间段内依照给定的采样率对某种潜在过程进行观测的结果。时间序列数据本质上反映的是某个或者某些随机变量随时间不断变化的趋势,而时间序列预测方法的核心就是从数据中挖掘出这种规律,并利用其对将来的数据做出估计。针对时间序列预测方法,着重介绍了传统的时间序列预测方法、基于机器学习的时间序列预测方法和基于参数模型的在线时间序列预测方法,并对未来的研究方向进行了进一步的展望。

关键词: 机器学习, 时间序列, 时间序列预测, 在线学习

Abstract: Time series is a set of random variables ordered in timestamp.It is often the observation of an underlying process,in which values are collected from uniformly spaced time instants,according to a given sampling rate.Time series data essentially reflects the trend that one or some random variables change with time.The core of time series prediction is mining the rule from data and making use of it to estimate future data.This paper emphatically introduced a summary of time series prediction method,namely the traditional time series prediction method,machine learning based time series prediction method and online time series prediction method based on parameter model,andfurther prospected the future research direction.

Key words: Machine learning, Online learning, Time series, Time series prediction


  • TP181
[1]YUAN J D,WANG Z H.Review of Time Series Representation and Classification Techniques [J].Computer Science,2015,42(3):1-7.(in Chinese)<br /> 原继东,王志海.时间序列的表示与分类算法综述[J].计算机科学,2015,42(3):1-7.<br /> [2]GAO J,SULTAN H,HU J,et al.Denoising Nonlinear Time Series by Adaptive Filtering and Wavelet Shrinkage:A Comparison [J].IEEE Signal Processing Letters,2010,17(3):237-240.<br /> [3]ROJO-ALVAREZ J L,MARTINEZ-RAMON M,PRADO-CUMPLIDO M,et al.Support Vector Method for Robust ARMA System Identification [J].IEEE Transactions on Signal Processing,2004,52(1):155-164.<br /> [4]GRANGER C W J,NEWBOLD P.Forecasting Economic Time Series [M].New York:Academic Press,1986.<br /> [5]BOX G,JENKINS G.Time Series Analysis,Forecasting and Control [M].Holden-Day,1990.<br /> [6]HAMILTON J.Time Series Analysis [M].Princeton:Princeton University Press,1994.<br /> [7]DEMPSTER A P,LAIRD N M,RUBIN D B.Maximum Likelihood from Incomplete Data via the EM Algorithm [J].Journal of the Royal Statistical Society,Series B (Methodological),1977,39(1):1-38.<br /> [8]DURBIN J,KOOPMAN S J.Time Series Analysis by State Space Methods [M].Oxford:Oxford University Press,2012.<br /> [9]KALMAN R E.A New Approach to Linear Filtering and Prediction Problems [J].Journal of Fluids Engineering,1960,82(1):35-45.<br /> [10]ALQUIER P,LI X,WINTENBERGER O.Prediction of Time Series by Statistical Learning:General Losses and Fast Rates [J].Dependence Modelling,2014,1:65-93.<br /> [11]KUZNETSOV V,MOHRI M.Generalization Bounds for Time Series Prediction with Non-Stationary Processes [M]//Algorithmic Learning Theory.Springer International Publishing,2014:260-274.<br /> [12]CRISTIANINI N,TAYLOR J S.Introduction to Support Vector Machines [M].李国正,王猛,曾华军,译.北京:电子工业出版社,2004.<br /> [13]ZHANG X G.Introduction to Statistical Learning Theory and Support Vector Machines [J].Acta Automatica Sinica,2000,26(1):32-41.(in Chinese)<br /> 张学工.关于统计学习理论与支持向量机 [J].自动化学报,2000,26(1):32-41.<br /> [14]VAPNIK V N.The Nature of Statistical Learning Theory [M].张学工,译.北京:清华大学出版社,2000.<br /> [15]VAPNIK V N.Statistical Learning Theory [M].许建华,张学工,译.北京:电子工业出版社,2004.<br /> [16]CRISTIANINI N,TAYLOR J S.An Introduction to Support Vector Machines [M].Cambridge:Cambridge University Press,2000.<br /> [17]KIM K.Financial Time Series Forecasting Using Support Vector Machines [J].Neurocomputing,2003,55(1):307-319.<br /> [18]GESTEL T V,SUYKENS J A K,BAESTAENS D E,et al.Financial Time Series Prediction Using Least Squares Support Vector Machines within The Evidence Framework [J].IEEE Transactions on Neural Networks,2001,12(4),809-821.<br /> [19]MACKAY D J C.Bayesian Interpolation [J].Neural Computation,1992,4(3):415-447.<br /> [20]MACKAY D J C.Probable Networks and Plausible Predictions-A Review of Practical Bayesian Methods for Supervised Neural Networks [J].Network Computation in Neural Systems,1995,6(3):469-505.<br /> [21]SUYKENS J A K,VANDEWALLE J.Least Squares Support Vector Machine Classifiers [J].Neural Processing Letters,1999,9(3):293-300.<br /> [22]SUYKENS J A K.Least Squares Support Vector Machines for Classification and Nonlinear Modeling [J].Neural Network World,2000,10(1):29-48.<br /> [23]MELLIT A,PAVAN A M,BENGHANEM M.Least Squares Support Vector Machine For Short-Term Prediction of Meteo-rological Time Series [J].Theoretical Applied Climatology,2013,111(1):297-307.<br /> [24]TAY F E H,CAO L.Application of Support Vector Machines in Financial Time Series Forecasting [J].Omega,2001,29(4):309-317.<br /> [25]JACOBS R A,JORDAN M A,NOWLAN S J,et al.Adaptive Mixtures of Local Experts [J].Neural Computation,1991,3(1):79-87.<br /> [26]JORDAN M I,JACOBS R A.Hierarchical Mixtures of Experts and the EM Algorithm [J].Neural Computation,1994,6(2):181-214.<br /> [27]WEIGEND A S,MANAGEAS M.Analysis and Prediction of Multi-Stationary Time Series [C]//Proceedings of the Third International Conference on Neural Networks in the Capital Markets.1995.<br /> [28]WEIGEND A S,MANAGEAS M,SRIVASTAVA A N.Nonli-near Gated Experts for Time Series:Discovering Regimes and Avoiding Over-fitting [J].International Journal of Neural Systems,1995,6(4):373-399.<br /> [29]KOHONEN T.Self-Organization and Associative Memory[M]. Springer,1989.<br /> [30]PEARL J.Fusion,Propagation and Structuring In Belief Networks [J].Artificial Intelligence,1986,2(3):241-288.<br /> [31]COOPER G F,HERSKOVITS E.A Bayesian Method for the Induction of Probabilistic Networks from Data [J].Machine Learning,2008,9(4):309-347.<br /> [32]ZHANG M,ZHOU Z.Multi-Label Neural Networks with Application to Function Genomics and Text Categorization [J].IEEE Transactions on Knowledge and Data Engineering,2006,18(10):1338-1351.<br /> [33]MEZ J,MATEO J L,PUERTA J.Learning Bayesian Networks by Hill Climbing:Efficient Methods Based on Progressive Restriction of the Neighborhood [J].Data Mining Knowledge Discovery,2011,22(1-2):106-148.<br /> [34]DAS M,GHOSH S K.A Probabilistic Approach for Weather Forecast Using Spatio-temporal Inter-relationship among Climate Variables [C]//International Conference on Industrial and Information Systems.IEEE,2015.<br /> [35]DAS M,GHOSH S K.SemBnet:A Semantic Bayesian Network for Multivariate Prediction of Meteorological Time Series Data [J].Pattern Recognition Letters,2017,93:192-201.<br /> [36]KOREN Y,BELL R,VOLINSKY C.Matrix Factorization Techniques for Recommender Systems [J].Computer,2009,42(8):30-37.<br /> [37]KOREN K.Factorization Meets The Neighborhood:A Multifa-ceted Collaborative Filtering Model [C]//ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.ACM,2008:426-434.<br /> [38]SALAKHUTDINOV R,MNIH A.Probabilistic Matrix Factorization [C]//International Conference on Neural Information Processing Systems.Curran Associates Inc.,2007:1257-1264.<br /> [39]XU M,ZHU J,ZHANG B.Bayesian Nonparametric Maximum Margin Matrix Factorization for Collaborative Prediction [C]//Advances in Neural Information Processing Systems.2012.<br /> [40]SREBRO N.Maximum margin matrix factorization[J].Advances in Nips,2005,37(2):1329-1336.<br /> [41]BALAKRISHNAN S,CHOPRA S.Collaborative Ranking[C]//ACM International Conference on Web Search and Data Mi-ning.ACM,2012:143-152.<br /> [42]WEIMER M,KARATZOGLOU A,LE Q,et al.CoFiRank—Maximum Margin Matrix Factorization for Collaborative Ran-king [C]//Neural Information Processing Systems.2007:1593-1600.<br /> [43]KROHN-GRIMBERGHE A,DRUMOND L,FREUDENTHALER C.Multi-Relational Matrix Factorization Using Bayesian Personalized Ranking for Social Network Data [C]//Procee-dings of the Fifth International Conference on Web Search and Web Data Mining(WSDM 2012).Seattle,WA,USA,2012.<br /> [44]ZHANG Y,ROUGHAN M,WILLINGER W,et al.Spatio-temporal Compressive Sensing and Internet Traffic Matrices [C]//ACM Sigcomm Conference on Data Communication.2009.<br /> [45]RALLAPALLI S,QIU L,ZHANG Y,et al.Exploiting Temporal Stability and Low-Rank Structure for Localization in Mobile Networks [C]//International Conference on Mobile Computing and Networking(Mobicom’10).2010.<br /> [46]YU H F,RAO N,DHILLON I S.Temporal Regularized Matrix Factorization for High-Dimensional Time Series Prediction [C]//NIPS.2016.<br /> [47]XIONG L,CHEN X,HUANG T K,et al.Temporal Collaborative Filtering with Bayesian Probabilistic Tensor Factorization [C]//Siam International Conference on Data Mining(SDM 2010).Columbus,Ohio,USA,DBLP,2010:211-222.<br /> [48]RASMUSSEN C E,WILLIAMS C K I.Gaussian Processes for Machine Learning [M].The MIT Press,2006.<br /> [49]ALEXE B,DESELAERS T,FERRARI V.What is An Object [C]//Computer Vision and Pattern Recognition.IEEE,2010:73-80.<br /> [50]EVERINGHAM M,GOOL L V,WILLIAMS C K I,et al.The Pascal,Visual Object Classes (VOC) Challenge[J].Internatio-nal Journal of Computer Vision,2010,88(2):303-338.<br /> [51]FELZENSZWALB P F,GIRSHICK R B,MCALLESTER D,et al.Object Detection with Discriminatively Trained Part-Based Models [J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2010,32(9):1627-1645.<br /> [52]KAPOOR A,GRAUMAN K,URTASUNR,et al.Gaussian Processes for Object Categorization[J].International Journal of Computer Vision,2010,88(2):169-188.<br /> [53]CHUM O,ZISSERMAN A.An Exemplar Model for Learning Object Classes [C]//IEEE Conference on Computer Vision and Pattern Recognition,2007(CVPR’07).IEEE,2007:1-8.<br /> [54]DIOSAN L,ROGOZAN A,PECUCHET J P.Evolving Kernel Functions for SVMs by Genetic Programming [C]//International Conference on Machine Learning and Applications.IEEE,2007:19-24.<br /> [55]WU B,ZHANG W,CHEN L,et al.A GP-based Kernel Construction and Optimization Method for RVM [C]//The International Conference on Computer and Automation Engineering.IEEE,2010:419-423.<br /> [56]KLENSKE E D,ZEILINGER M N,SCHOLKOPF B,et al. Nonparametric Dynamics Estimation for Time Periodic Systems [C]//Communication,Control,and Computing.IEEE,2014:486-493.<br /> [57]LLOYD J R,DUVENAUD D,GROSSE R,et al.Automatic Construction and Natural-Language Description of Nonparame-tric Regression Models [C]//Twenty-Eighth AAAI Conference on Artificial Intelligence.AAAI Press,2014:1242-1250.<br /> [58]TOBAR F,BUI T D,TURNER R E.Learning Stationary Time Series Using Gaussian Processes with Nonparametric Kernels [C]//Advances in Neural Information Processing Systems 28 (NIPS 2015).2015.<br /> [59]HWANG Y,TONG A,CHOI J.Automatic Construction of Nonparametric Relational Regression Models for Multiple Time Series [C]//International Conference on International Confe-rence on Machine Learning.JMLR.org,2016:3030-3039.<br /> [60]KUREMOTO T,KIMURA S,KOBAYASHI K,et al.Time Series Forecasting Using a Deep Belief Network with Restricted Boltzman Machines [J].Neurocomputing,2014,137(15):47-56.<br /> [61]TURNER J T.Time Series Analysis Using Deep Feed Forward Neural Networks [D].Baltimore:University of Maryland,2014.<br /> [62]ROMEU P,ZAMORA-MARTINEZ F,BOTELLA-ROCAMO-RA P,et al.Time-Series Forecasting of Indoor Temperature Using Pre-trained Deep Neural Networks[C]//International Conference on Artificial Neural Networks.Berlin:Springer,2013:451-458.<br /> [63]LV Y,DUAN Y,KANG W,et al.Traffic Flow Prediction with Big Data:A Deep Learning Approach [J].IEEE Transactions on Intelligent Transportation Systems,2015,16(2):865-873.<br /> [64]LANGKVIST M,KARLSSON L,LOUTFI A.A Review of Un-supervised Feature Learning and Deep Learning for Time-Series Modeling [J].Pattern Recognition Letters,2014,42(1):11-24.<br /> [65]GAMBOA J C B.Deep Learning for Time-Series Analysis [J].Arxiv:1701.01887,2017.<br /> [66]HEATON J B,POLSON N G,WITTE J H.Deep Learning in Finance [J].Arxiv:1602.06561,2016.<br /> [67]BOROVYKH A,BOHTE S,OOSTERLEE C W.Conditional Time Series Forecasting with Convolutional Neural Networks [J].Arxiv:1703.04691,2017.<br /> [68]OORD A,DIELEMAN S,ZEN H,et al.Wavenet:A Generative Model for Raw Audio [J].Arxiv:1609.03499,2016.<br /> [69]JAIN A,KUMAR A M.Hybrid Neural Network Models for Hydrologic Time Series Forecasting [J].Applied Soft Computing,2007,7(2):585-592.<br /> [70]ZHANG G,PATUWO B E,HU M.Forecasting with Artificial Neural Networks:The State of The Art [J].International Journal of Forecasting,1998,14(1):35-62.<br /> [71]ZHANG G.Time Series Forecasting Using a Hybrid ARIMA and Neural Network Model [J].Neurocomputing,2003,50(1):159-175.<br /> [72]BINKOWSKI M,MARTI G,DONNAT P.Autoregressive Convolutional Neural Networks for Asynchronous Time Series [J].Arxiv:1703.04122,2017.<br /> [73]GOEL H,MELNYK I,BANERJEE A.R2N2:Residual Recurrent Neural Networks for Multivariate Time Series Forecasting [J].Arxiv:1709.03159,2017.<br /> [74]LAI G,CHANG W,YANG Y,et al.Modeling Long- And Short-Term Temporal Patterns with Deep Neural Networks [J].Ar-xiv:1703.07015,2017.<br /> [75]ANAVA O,HAZAN E,MANNOR S,et al.Online Learning for Time Series Prediction[J].Journal of Machine Learning Research,2013,30:172-184.<br /> [76]SHWARTZ S S.Online Learning and Online Convex Optimization [J].Foundations and Trends in Machine Learning,2011,4(2):107-194.<br /> [77]ZINKEVICH M.Online Convex Programming and Generalized Infinitesimal Gradient Ascent [C]//Proceedings of the Twen-tieth International Conference on Machine Learning (ICML).2003:928-936.<br /> [78]HAZAN E,AGARWAL A,KALE S.Logarithmic regret algorithms for online convex optimization[J].Machine Learning,2007,69(2-3):169-192.
[1] 冷典典, 杜鹏, 陈建廷, 向阳.
Automated Container Terminal Oriented Travel Time Estimation of AGV
计算机科学, 2022, 49(9): 208-214. https://doi.org/10.11896/jsjkx.210700028
[2] 宁晗阳, 马苗, 杨波, 刘士昌.
Research Progress and Analysis on Intelligent Cryptology
计算机科学, 2022, 49(9): 288-296. https://doi.org/10.11896/jsjkx.220300053
[3] 李瑶, 李涛, 李埼钒, 梁家瑞, Ibegbu Nnamdi JULIAN, 陈俊杰, 郭浩.
Construction and Multi-feature Fusion Classification Research Based on Multi-scale Sparse Brain Functional Hyper-network
计算机科学, 2022, 49(8): 257-266. https://doi.org/10.11896/jsjkx.210600094
[4] 张光华, 高天娇, 陈振国, 于乃文.
Study on Malware Classification Based on N-Gram Static Analysis Technology
计算机科学, 2022, 49(8): 336-343. https://doi.org/10.11896/jsjkx.210900203
[5] 何强, 尹震宇, 黄敏, 王兴伟, 王源田, 崔硕, 赵勇.
Survey of Influence Analysis of Evolutionary Network Based on Big Data
计算机科学, 2022, 49(8): 1-11. https://doi.org/10.11896/jsjkx.210700240
[6] 陈明鑫, 张钧波, 李天瑞.
Survey on Attacks and Defenses in Federated Learning
计算机科学, 2022, 49(7): 310-323. https://doi.org/10.11896/jsjkx.211000079
[7] 高振卓, 王志海, 刘海洋.
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
[8] 王飞, 黄涛, 杨晔.
Study on Machine Learning Algorithms for Life Prediction of IGBT Devices Based on Stacking Multi-model Fusion
计算机科学, 2022, 49(6A): 784-789. https://doi.org/10.11896/jsjkx.210400030
[9] 李亚茹, 张宇来, 王佳晨.
Survey on Bayesian Optimization Methods for Hyper-parameter Tuning
计算机科学, 2022, 49(6A): 86-92. https://doi.org/10.11896/jsjkx.210300208
[10] 赵璐, 袁立明, 郝琨.
Review of Multi-instance Learning Algorithms
计算机科学, 2022, 49(6A): 93-99. https://doi.org/10.11896/jsjkx.210500047
[11] 肖治鸿, 韩晔彤, 邹永攀.
Study on Activity Recognition Based on Multi-source Data and Logical Reasoning
计算机科学, 2022, 49(6A): 397-406. https://doi.org/10.11896/jsjkx.210300270
[12] 姚烨, 朱怡安, 钱亮, 贾耀, 张黎翔, 刘瑞亮.
一种基于异质模型融合的 Android 终端恶意软件检测方法
Android Malware Detection Method Based on Heterogeneous Model Fusion
计算机科学, 2022, 49(6A): 508-515. https://doi.org/10.11896/jsjkx.210700103
[13] 刘宝宝, 杨菁菁, 陶露, 王贺应.
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
[14] 许杰, 祝玉坤, 邢春晓.
Application of Machine Learning in Financial Asset Pricing:A Review
计算机科学, 2022, 49(6): 276-286. https://doi.org/10.11896/jsjkx.210900127
[15] 么晓明, 丁世昌, 赵涛, 黄宏, 罗家德, 傅晓明.
Big Data-driven Based Socioeconomic Status Analysis:A Survey
计算机科学, 2022, 49(4): 80-87. https://doi.org/10.11896/jsjkx.211100014
Full text



No Suggested Reading articles found!