Computer Science ›› 2023, Vol. 50 ›› Issue (6A): 220600194-12.doi: 10.11896/jsjkx.220600194
• Artificial Intelligence • Previous Articles Next Articles
CAO Zhihao1, MU Shaomin2, QU Hongchun1
CLC Number:
| [1]ADLER J,PARMRYD I.Quantifying colocalization by correlation:the Pearson correlation coefficient is superior to the Mander’s overlap coefficient[J].Cytometry Part A,2010,77(8):733-742. [2]SUGIHARA G,MAY R,YE H,et al.Detecting causality incomplex ecosystems[J].science,2012,338(6106):496-500. [3]ALTMAN N,KRZYWINSKI M.Points of Significance:Asso-ciation,correlation and causation[J].Nature Methods,2015,12(10):899-900. [4]HUANG Y,FU Z,FRANZKE C L E.Detecting causality from time series in a machine learning framework[J].Chaos:An Interdisciplinary Journal of Nonlinear Science,2020,30(6):063116. [5]PEARL J,MACKENZIE D.The book of why:the new science of cause and effect[M].Basic Books,2018. [6]CAO Z,MU S,XU L,et al.Causal Research on Soil Temperature and Moisture Content at Different Depths[J].IEEE Access,2021,9:39077-39088. [7]PEARL J.Theoretical impediments to machine learning withseven sparks from the causal revolution[J].arXiv:1801.04016,2018. [8]GRANGER C W J.Investigating causal relations by econometric models and cross-spectral methods[J].Econometrica:Journal of the Econometric Society,1969:424-438. [9]PEARL J.Causal inference in statistics:An overview[J].Statistics surveys,2009,3:96-146. [10]LINDEGREN M,CHECKLEY D M,ROUYER T,et al.Climate,fishing,and fluctuations of sardine and anchovy in the California Current[J].Proceedings of the National Academy of Sciences,2013,110(33):13672-13677. [11]PARK J,PAO G M,SUGIHARA G,et al.Empirical mode mo-deling[J].Nonlinear Dynamics,2022:108(3):2147-2160. [12]YE H,BEAMISH R J,GLASER S M,et al.Equation-freemechanistic ecosystem forecasting using empirical dynamic mo-deling[J].Proceedings of the National Academy of Sciences,2015,112(13):E1569-E1576. [13]PAGANO R R.Understanding statistics in the behavioralsciences[M].Cengage Learning,2012. [14]SIMON H A.Spurious correlation:A causal interpretation[J].Journal of the American statistical Association,1954,49(267):467-479. [15]GUO F,TIAN L W.Correlation and causality[J].NationalMedical Journal of China,2019(10):790-795. [16]MA H,LENG S,CHEN L.Data-based prediction and causality inference of nonlinear dynamics[J].Science China Mathematics,2018,61(3):403-420. [17]WARD A.Spurious correlations and causal inferences[J].Erkenntnis,2013,78(3):699-712. [18]GLYMOUR M,PEARL J,JEWELL N P.Causal inference in statistics:A primer[M].John Wiley & Sons,2016. [19]MORAN P A P.The statistical analysis of the Canadian lynxcycle[J].Australian Journal of Zoology,1953,1(3):291-298. [20]PANCZAK R,GALOBARDES B,SPOERRI A,et al.High life in the sky? Mortality by floor of residence in Switzerland[J].European Journal of Epidemiology,2013,28(6):453-462. [21]LORENZ E N.Deterministic nonperiodic flow[J].Journal of Atmospheric Sciences,1963,20(2):130-141. [22]DEANGELIS D L,YUREK S.Equation-free modeling unravels the behavior of complex ecological systems[J].Proceedings of the National Academy of Sciences,2015,112(13):3856-3857. [23]TAKENS F.Detecting strange attractors in turbulence[M]//Dynamical Systems and Turbulence,Warwick 1980.Springer,1981:366-381. [24]DEYLE E R,SUGIHARA G.Generalized theorems for nonli-near state space reconstruction[J].Plos One,2011,6(3):e18295. [25]KANTZ H,SCHREIBER T.Nonlinear time series analysis[M].Cambridge University Press,2004. [26]BRUNTON S L,PROCTOR J L,KUTZ J N.Discovering governing equations from data by sparse identification of nonlinear dynamical systems[J].Proceedings of the National Academy of Sciences,2016,113(15):3932-3937. [27]STROGATZ S H.Nonlinear dynamics and chaos:with applications to physics,biology,chemistry,and engineering[M].CRC press,2018. [28]ASCIOTI F A,BELTRAMI E,CARROLL T O,et al.Is there chaos in plankton dynamics?[J].Journal of Plankton Research,1993,15(6):603-617. [29]PACKARD N H,CRUTCHFIELD J P,FARMER J D,et al.Geometry from a time series[J].Physical Review Letters,1980,45(9):712. [30]LIEBERT W,SCHUSTER H G.Proper choice of the time delay for the analysis of chaotic time series[J].Physics Letters A,1989,142(2/3):107-111. [31]PECORA L M,MONIZ L,NICHOLS J,et al.A unified ap-proach to attractor reconstruction[J].Chaos:An Interdisciplinary Journal of Nonlinear Science,2007,17(1):013110. [32]KENNEL M B,BROWN R,ABARBANEL H D I.Determining embedding dimension for phase-space reconstruction using a geometrical construction[J].Physical Review A,1992,45(6):3403. [33]SUGIHARA G,MAY R M.Nonlinear forecasting as a way of distinguishing chaos from measurement error in time series[J].Nature,1990,344(6268):734-741. [34]FARMER J D,SIDOROWICH J J.Predicting chaotic time series[J].Physical Review Letters,1987,59(8):845. [35]WHITNEY H.Differentiable manifolds[J].Annals of Mathe-matics,1936:645-680. [36]CHANG C W,USHIO M,HSIEH C.Empirical dynamic mode-ling for beginners[J].Ecological Research,2017,32(6):785-796. [37]HSIEH C,GLASER S M,LUCAS A J,et al.Distinguishing random environmental fluctuations from ecological catastrophes for the North Pacific Ocean[J].Nature,2005,435(7040):336-340. [38]DIXON P A,MILICICH M J,SUGIHARA G.Episodic fluctuations in larval supply[J].Science,1999,283(5407):1528-1530. [39]YE H,SUGIHARA G.Information leverage in interconnectedecosystems:Overcoming the curse of dimensionality[J].Scien-ce,2016,353(6302):922-925. [40]SUGIHARA G.Nonlinear forecasting for the classification ofnatural time series[J].Philosophical Transactions of the Royal Society of London.Series A:Physical and Engineering Sciences,1994,348(1688):477-495. [41]KRAKOVSKÁ A,JAKUBÍK J.Implementation of two causal methods based on predictions in reconstructed state spaces[J].Physical Review E,2020,102(2):022203. [42]MA H,LENG S,AIHARA K,et al.Randomly distributed embedding making short-term high-dimensional data predictable[J].Proceedings of the National Academy of Sciences,2018,115(43):E9994-E10002. [43]CUMMINS B,GEDEON T,SPENDLOVE K.On the efficacy of state space reconstruction methods in determining causality[J].SIAM Journal on Applied Dynamical Systems,2015,14(1):335-381. [44]ARNOLD A,LIU Y,ABE N.Temporal causal modeling with graphical granger methods[C]//Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.2007:66-75. [45]LE VAN QUYEN M,MARTINERIE J,ADAM C,et al.Nonlinear analyses of interictal EEG map the brain interdependences in human focal epilepsy[J].Physica D:Nonlinear Phenomena,1999,127(3/4):250-266. [46]DAVID O,GUILLEMAIN I,SAILLET S,et al.Identifying neural drivers with functional MRI:an electrophysiological validation[J].PLoS biology,2008,6(12):e315. [47]HLAVÁČKOVÁ-SCHINDLER K,PALUŠ M,VEJMELKA M,et al.Causality detection based on information-theoretic approaches in time series analysis[J].Physics Reports,2007,441(1):1-46. [48]PALUŠ M,KRAKOVSKÁ A,JAKUBÍK J,et al.Causality,dynamical systems and the arrow of time[J].Chaos:An Interdisciplinary Journal of Nonlinear Science,2018,28(7):075307. [49]MCCRACKEN J M,WEIGEL R S.Convergent cross-mapping and pairwise asymmetric inference[J].Physical Review E,2014,90(6):062903. [50]HSIEH C,OHMAN M D.Biological responses to environmental forcing:the linear tracking window hypothesis[J].Ecology,2006,87(8):1932-1938. [51]MØNSTER D,FUSAROLI R,TYLÉN K,et al.Causal infe-rence from noisy time-series data－testing the convergent cross-mapping algorithm in the presence of noise and external in-fluence[J].Future Generation Computer Systems,2017,73:52-62. [52]SCHIFF S J,SO P,CHANG T,et al.Detecting dynamical interdependence and generalized synchrony through mutual prediction in a neural ensemble[J].Physical Review E,1996,54(6):6708. [53]COUFAL D,JAKUBÍK J,JAJCAY N,et al.Detection of coupling delay:A problem not yet solved[J].Chaos:An Interdisciplinary Journal of Nonlinear Science,2017,27(8):083109. [54]YE H,DEYLE E R,GILARRANZ L J,et al.Distinguishingtime-delayed causal interactions using convergent cross mapping[J].Scientific Reports,2015,5(1):1-9. [55]GE X,LIN A.Dynamic causality analysis using overlapped sli-ding windows based on the extended convergent cross-mapping[J].Nonlinear Dynamics,2021,104(2):1753-1765. [56]MA H,AIHARA K,CHEN L.Detecting causality from nonli-near dynamics with short-term time series[J].Scientific Reports,2014,4(1):1-10. [57]MA H,LENG S,TAO C,et al.Detection of time delays and directional interactions based on time series from complex dynamical systems[J].Physical Review E,2017,96(1):012221. [58]HARNACK D,LAMINSKI E,SCH?NEMANN M,et al.Topological causality in dynamical systems[J].Physical Review Letters,2017,119(9):098301. [59]WANG Y,HU F,CAO Y,et al.Improved CCM for variablecausality detection in complex systems[J].Control Engineering Practice,2019,83:67-82. [60]DÍAZ E,ADSUARA J E,MARTÍNEZ Á M,et al.Inferringcausal relations from observational long-term carbon and water fluxes records[J].Scientific Reports,2022,12(1):1-12. [61]CLARK A T,YE H,ISBELL F,et al.Spatial convergent cross mapping to detect causal relationships from short time series[J].Ecology,2015,96(5):1174-1181. [62]KRAKOVSKÁ A,HANZELY F.Testing for causality in reconstructed state spaces by an optimized mixed prediction method[J].Physical Review E,2016,94(5):052203. [63]LENG S,MA H,KURTHS J,et al.Partial cross mapping eliminates indirect causal influences[J].Nature communications,2020,11(1):1-9. [64]JOHNSON B,MUNCH S B.An empirical dynamic modelingframework for missing or irregular samples[J].Ecological Mo-delling,2022,468:109948. [65]JIANG J J,HUANG Z G,HUANG L,et al.Directed dynamical influence is more detectable with noise[J].Scientific Reports,2016,6(1):1-9. [66]KAWATSU K,USHIO M,VAN VEEN F J F,et al.Are networks of trophic interactions sufficient for understanding the dynamics of multi-trophic communities? Analysis of a tri-trophic insect food-web time-series[J].Ecology Letters,2021,24(3):543-552. [67]MCGOWAN J A,DEYLE E R,YE H,et al.Predicting coastal algal blooms in southern California[J].Ecology,2017,98(5):1419-1433. [68]YE L,TAN L,WU X,et al.Nonlinear causal analysis reveals an effective water level regulation approach for phytoplankton blooms controlling in reservoirs[J].Science of The Total Environment,2022,806:150948. [69]USHIO M,HSIEH C,MASUDA R,et al.Fluctuating interaction network and time-varying stability of a natural fish community[J].Nature,2018,554(7692):360-363. [70]CHANG C W,MIKI T,YE H,et al.Causal networks of phytoplankton diversity and biomass are modulated by environmental context[J].Nature Communications,2022,13(1):1-11. [71]KITAYAMA K,USHIO M,AIBA S I.Temperature is a dominant driver of distinct annual seasonality of leaf litter production of equatorial tropical rain forests[J].Journal of Ecology,2021,109(2):727-736. [72]SHI H,ZHAO Y,LIU S,et al.A new perspective on drought propagation:Causality[J].Geophysical Research Letters,2022,49(2):e2021GL096758. [73]DOI H,YASUHARA M,USHIO M.Causal analysis of thetemperature impact on deep-sea biodiversity[J].Biology Letters,2021,17(7):20200666. [74]WEN C,YANG J.Complexity evolution of chaotic financial systems based on fractional calculus[J].Chaos,Solitons & Fractals,2019,128:242-251. [75]WANG Z,HUANG X,SHI G.Analysis of nonlinear dynamics and chaos in a fractional order financial system with time delay[J].Computers & Mathematics with Applications,2011,62(3):1531-1539. [76]HUFFAKER R,FEARNE A.Empirically testing for dynamiccausality between promotions and sales beer promotions and sales in england[C]//2014 International European Forum.2014:17-21. [77]AZQUETA-GAVALDN A.Causal inference between cryp-tocurrency narratives and prices:Evidence from a complex dynamic ecosystem[J].Physica A:Statistical Mechanics and its Applications,2020,537:122574. [78]VISSIO G,LUCARINI V.A proof of concept for scale-adaptive parametrizations:the case of the Lorenz’96 model[J].Quarterly Journal of the Royal Meteorological Society,2018,144(710):63-75. [79]TSONIS A A,DEYLE E R,MAY R M,et al.Dynamical evidence for causality between galactic cosmic rays and interannual variation in global temperature[J].Proceedings of the National Academy of Sciences,2015,112(11):3253-3256. [80]WANG X,PIAO S,CIAIS P,et al.A two-fold increase of carbon cycle sensitivity to tropical temperature variations[J].Nature,2014,506(7487):212-215. [81]VAN NES E H,SCHEFFER M,BROVKIN V,et al.Causalfeedbacks in climate change[J].Nature Climate Change,2015,5(5):445-448. [82]WANG Y,YANG J,CHEN Y,et al.Detecting the causal effect of soil moisture on precipitation using convergent cross mapping[J].Scientific Reports,2018,8(1):1-8. [83]HUANG Y,FRANZKE C L E,YUAN N,et al.Systematicidentification of causal relations in high-dimensional chaotic systems:application to stratosphere-troposphere coupling[J].Climate Dynamics,2020,55(9):2469-2481. [84]ZHANG N,WANG G.Detecting the causal interaction between Siberian high and winter surface air temperature over northeast Asia[J].Atmospheric Research,2020,245:105066. [85]HESKAMP L,MEEL-VAN DEN ABEELEN A S,LAGRO J,et al.Convergent cross mapping:a promising technique for cerebral autoregulation estimation[J].Int.J.Clin.Neurosci.Ment.Heal.S,2014,20. [86]MCBRIDE J C,ZHAO X,MUNRO N B,et al.Sugihara causality analysis of scalp EEG for detection of early Alzheimer’s disease[J].NeuroImage:Clinical,2015,7:258-265. [87]DEYLE E R,MAHER M C,HERNANDEZ R D,et al.Global environmental drivers of influenza[J].Proceedings of the National Academy of Sciences,2016,113(46):13081-13086. [88]MÜLLER A,KRAEMER J F,PENZEL T,et al.Causality inphysiological signals[J].Physiological Measurement,2016,37(5):R46. [89]VERMA A K,GARG A,XU D,et al.Skeletal muscle pump drives control of cardiovascular and postural systems[J].Scientific Reports,2017,7(1):1-8. [90]COBEY S,BASKERVILLE E B.Limits to causal inference with state-space reconstruction for infectious disease[J].PloS One,2016,11(12):e0169050. [91]BOZORGMAGHAM A E,MOTESHARREI S,PENNY S G,et al.Causality analysis:identifying the leading element in a coupled dynamical system[J].PLoS One,2015,10(6):e0131226. [92]LUO L,CHENG F,QIU T,et al.Refined convergent cross-mapping for disturbance propagation analysis of chemical processes[J].Computers & Chemical Engineering,2017,106:1-16. [93]XIANG S,BAI Y,ZHAO J.Medium-term prediction of keychemical process parameter trend with small data[J].Chemical Engineering Science,2022,249:117361. |
| [1] | YE Song-tao, ZHOU Yang-zheng, FAN Hong-jie, CHEN Zheng-lei. Joint Learning of Causality and Spatio-Temporal Graph Convolutional Network for Skeleton- based Action Recognition [J]. Computer Science, 2021, 48(11A): 130-135. |
| [2] | WU Wen-jun, YU Xin, PU Yan-jun, WANG Qun-bo, YU Xiao-ming. Development of Complex Service Software in Microservice Era [J]. Computer Science, 2020, 47(12): 11-17. |
| [3] | CHEN Qian, SHE Wei and YE Yang-dong. Method of Behavior Analysis for Complex System Based on Hierarchical Bayesian Petri Net with Time Factor [J]. Computer Science, 2015, 42(7): 62-67. |
| [4] | PENG Zhen,TIAN Li-qin,WU Jing,GAO Xiao-yan and YANG Bing-ru. Research on Complex System Modeling and Reasoning Based on Large Fuzzy Cognitive Map [J]. Computer Science, 2013, 40(6): 203-205. |
| [5] | . Multiple Paths Test Case Generation Based on Complex System Genetic Algorithm [J]. Computer Science, 2012, 39(4): 139-141. |
| [6] | XU Wen-shuan, XIN Yun-wei, LU Gui-zhang ,CHEN Qiu-shuang （College of Information Technical Science, Nankai University, Tianjin 300071, China）. [J]. Computer Science, 2009, 36(4): 246-249. |
| [7] | . [J]. Computer Science, 2009, 36(2): 26-29. |
| [8] | . [J]. Computer Science, 2008, 35(9): 296-300. |
| [9] | . [J]. Computer Science, 2007, 34(4): 129-132. |
| [10] | . [J]. Computer Science, 2006, 33(7): 213-216. |
|
||
Home