Compression complexity with ordinal patterns for robust causal inference in irregularly sampled time series

[1]  Maximilian Göbel,et al.  On Spurious Causality, CO2, and Global Temperature , 2021, Econometrics.

[2]  M. Zanin,et al.  Ordinal patterns-based methodologies for distinguishing chaos from noise in discrete time series , 2021, Communications Physics.

[3]  D. Royer,et al.  A tighter constraint on Earth-system sensitivity from long-term temperature and carbon-cycle observations , 2021, Nature Communications.

[4]  F. Dekker,et al.  Conducting correlation analysis: important limitations and pitfalls , 2021, Clinical kidney journal.

[5]  Johannes Lederer.  Theory I: Prediction , 2021, Springer Texts in Statistics.

[6]  Philippe Goulet Coulombe,et al.  On Spurious Causality, CO2, and Global Temperature , 2021, 2103.10605.

[7]  G. Moore,et al.  Anomalous collapses of Nares Strait ice arches leads to enhanced export of Arctic sea ice , 2021, Nature communications.

[8]  D. Bae,et al.  Causal effects of Indian Ocean Dipole on El Niño–Southern Oscillation during 1950–2014 based on high-resolution models and reanalysis data , 2020, Environmental Research Letters.

[9]  Z. Kundzewicz,et al.  Atmospheric Temperature and CO2: Hen-Or-Egg Causality? , 2020, Sci.

[10]  S. Clemens,et al.  Methane, Monsoons, and Modulation of Millennial‐Scale Climate , 2020, Geophysical Research Letters.

[11]  R. K. Caldwell,et al.  Permutation , 2020, Definitions.

[12]  Peter M. Aronow,et al.  The Book of Why: The New Science of Cause and Effect , 2020, Journal of the American Statistical Association.

[13]  N. Nagaraj,et al.  Measuring Causality , 2019, Resonance.

[14]  Chisimkwuo John,et al.  Imputation of Missing Values in Economic and Financial Time Series Data Using Five Principal Component Analysis (PCA) Approaches , 2019, Central Bank of Nigeria Journal of Applied Statistics.

[15]  Bernhard Schölkopf,et al.  Inferring causation from time series in Earth system sciences , 2019, Nature Communications.

[16]  T. Lenton,et al.  Modelling the long-term carbon cycle, atmospheric CO2, and Earth surface temperature from late Neoproterozoic to present day , 2019, Gondwana Research.

[17]  Lei Wang,et al.  Estimating coupling strength between multivariate neural series with multivariate permutation conditional mutual information , 2019, Neural Networks.

[18]  Nithin Nagaraj,et al.  Data-based intervention approach for Complexity-Causality measure , 2018, PeerJ Prepr..

[19]  George Sugihara,et al.  Synchronization and causality across time scales in El Niño Southern Oscillation , 2018, npj Climate and Atmospheric Science.

[20]  M. Paluš,et al.  Central European air temperature: driving force analysis and causal influence of NAO , 2018, Theoretical and Applied Climatology.

[21]  W. Gough,et al.  Accounting for missing data in monthly temperature series: Testing rule‐of‐thumb omission of months with missing values , 2018, International Journal of Climatology.

[22]  R. Garreaud,et al.  Alternative approaches for estimating missing climate data: application to monthly precipitation records in South-Central Chile , 2018, Forest Ecosystems.

[23]  Mélanie Frappier,et al.  The Book of Why: The New Science of Cause and Effect , 2018, Science.

[24]  J. P. Grime,et al.  Severe effects of long-term drought on calcareous grassland seed banks , 2018, npj Climate and Atmospheric Science.

[25]  S. Arrhenius.  “On the Infl uence of Carbonic Acid in the Air upon the Temperature of the Ground” (1896) , 2017, The Future of Nature.

[26]  C. Deser,et al.  The role of the North Atlantic Oscillation in European climate projections , 2017, Climate Dynamics.

[27]  Riccardo Fusaroli,et al.  Causal inference from noisy time-series data - Testing the Convergent Cross-Mapping algorithm in the presence of noise and external influence , 2017, Future Gener. Comput. Syst..

[28]  Milan Paluš,et al.  Smooth information flow in temperature climate network reflects mass transport. , 2017, Chaos.

[29]  Filip Hanzely,et al.  Testing for causality in reconstructed state spaces by an optimized mixed prediction method. , 2016, Physical review. E.

[30]  Karthi Balasubramanian,et al.  Dynamical complexity of short and noisy time series , 2016, The European Physical Journal Special Topics.

[31]  Erik Laminski,et al.  Topological Causality in Dynamical Systems. , 2016, Physical review letters.

[32]  V. Brovkin,et al.  Interglacials of the last 800,000 years , 2016 .

[33]  Diego Macias,et al.  On the causal structure between CO2 and global temperature , 2016, Scientific Reports.

[34]  Herbert Witte,et al.  Convergent Cross Mapping: Basic concept, influence of estimation parameters and practical application , 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[35]  C. Masoller,et al.  A study of the air–sea interaction in the South Atlantic Convergence Zone through Granger causality , 2015 .

[36]  M. Scheffer,et al.  Causal feedbacks in climate change , 2015 .

[37]  A. Seth,et al.  Granger Causality Analysis in Neuroscience and Neuroimaging , 2015, The Journal of Neuroscience.

[38]  Thomas F. Stocker,et al.  Revision of the EPICA Dome C CO2 record from 800 to 600 kyr before present , 2015 .

[39]  Michael Small,et al.  Time lagged ordinal partition networks for capturing dynamics of continuous dynamical systems. , 2015, Chaos.

[40]  Karsten Keller,et al.  Ordinal Patterns, Entropy, and EEG , 2014, Entropy.

[41]  Jianping Li,et al.  A tree-ring reconstruction of the South Asian summer monsoon index over the past millennium , 2014, Scientific Reports.

[42]  Shaoli Wang,et al.  Bemisia tabaci Q carrying tomato yellow leaf curl virus strongly suppresses host plant defenses , 2014, Scientific Reports.

[43]  Rolf Larsson,et al.  What is the link between temperature and carbon dioxide levels? A Granger causality analysis based on ice core data , 2014, Theoretical and Applied Climatology.

[44]  Milan Paluš,et al.  Multiscale atmospheric dynamics: cross-frequency phase-amplitude coupling in the air temperature. , 2014, Physical review letters.

[45]  Dimitris Kugiumtzis,et al.  Simulation Study of Direct Causality Measures in Multivariate Time Series , 2013, Entropy.

[46]  Karthi Balasubramanian,et al.  A new complexity measure for time series analysis and classification , 2013, The European Physical Journal Special Topics.

[47]  Dimitris Kugiumtzis,et al.  Partial transfer entropy on rank vectors , 2013, ArXiv.

[48]  Viola Priesemann,et al.  Measuring Information-Transfer Delays , 2013, PloS one.

[49]  Badong Chen,et al.  Weighted-permutation entropy: a complexity measure for time series incorporating amplitude information. , 2013, Physical review. E, Statistical, nonlinear, and soft matter physics.

[50]  T. Dimpfl,et al.  Using transfer entropy to measure information flows between financial markets , 2013 .

[51]  M. Loutre,et al.  An optimized multi-proxy, multi-site Antarctic ice and gas orbital chronology (AICC2012): 120--800 ka , 2012 .

[52]  George Sugihara,et al.  Detecting Causality in Complex Ecosystems , 2012, Science.

[53]  D. Smirnov,et al.  Spurious causalities due to low temporal resolution: Towards detection of bidirectional coupling from time series , 2012 .

[54]  Massimiliano Zanin,et al.  Permutation Entropy and Its Main Biomedical and Econophysics Applications: A Review , 2012, Entropy.

[55]  H. Elderfield,et al.  Evolution of Ocean Temperature and Ice Volume Through the Mid-Pleistocene Climate Transition , 2012, Science.

[56]  Alessandro Attanasio,et al.  Testing for linear Granger causality from natural/anthropogenic forcings to global temperature anomalies , 2012, Theoretical and Applied Climatology.

[57]  S. S. Kozlenko,et al.  Relationship between El-Niño/Southern Oscillation and the Indian monsoon , 2012, Izvestiya, Atmospheric and Oceanic Physics.

[58]  Claude N. Williams,et al.  An overview of the Global Historical Climatology Network monthly mean temperature data set, version 3 , 2011 .

[59]  Snigdhansu Chatterjee,et al.  Exploring Granger causality between global average observed time series of carbon dioxide and temperature , 2011 .

[60]  E. Cook,et al.  Interdecadal modulation of El Nino amplitude during the past millennium , 2011 .

[61]  Juergen Kurths,et al.  Alternating mutual influence of El‐Niño/Southern Oscillation and Indian monsoon , 2011 .

[62]  G. Ouyang,et al.  Estimating coupling direction between neuronal populations with permutation conditional mutual information , 2010, NeuroImage.

[63]  Gordon Pipa,et al.  Transfer entropy—a model-free measure of effective connectivity for the neurosciences , 2010, Journal of Computational Neuroscience.

[64]  A. Engelen,et al.  Monthly, seasonal and annual temperature reconstructions for Central Europe derived from documentary evidence and instrumental records since AD 1500 , 2010 .

[65]  W. Landman.  Climate change 2007: the physical science basis , 2010 .

[66]  Jos Amig.  Permutation Complexity in Dynamical Systems: Ordinal Patterns, Permutation Entropy and All That , 2010 .

[67]  Matthäus Staniek,et al.  Symbolic transfer entropy: inferring directionality in biosignals , 2009, Biomedizinische Technik. Biomedical engineering.

[68]  James W. Hurrell,et al.  North Atlantic climate variability: The role of the North Atlantic Oscillation , 2009 .

[69]  E. Tracy,et al.  The application of the transfer entropy to gappy time series , 2009 .

[70]  Zhenyan Zhu,et al.  Economic growth and energy consumption revisited — Evidence from linear and nonlinear Granger causality , 2008 .

[71]  T. Stocker,et al.  High-resolution carbon dioxide concentration record 650,000–800,000 years before present , 2008, Nature.

[72]  T. Stocker,et al.  Orbital and millennial-scale features of atmospheric CH4 over the past 800,000 years , 2008, Nature.

[73]  Matthäus Staniek,et al.  Symbolic transfer entropy. , 2008, Physical review letters.

[74]  Daniele Marinazzo,et al.  Kernel method for nonlinear granger causality. , 2007, Physical review letters.

[75]  M. Paluš,et al.  Directionality of coupling from bivariate time series: how to avoid false causalities and missed connections. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[76]  C. Ropelewski,et al.  The Strengthening Relationship between ENSO and Northeast Monsoon Rainfall over Sri Lanka and Southern India , 2006 .

[77]  D. Stephenson,et al.  Granger Causality of Coupled Climate Processes: Ocean Feedback on the North Atlantic Oscillation , 2006 .

[78]  D. Maraun,et al.  Epochs of phase coherence between El Niño/Southern Oscillation and Indian monsoon , 2005 .

[79]  L. Kocarev,et al.  The permutation entropy rate equals the metric entropy rate for ergodic information sources and ergodic dynamical systems , 2005, nlin/0503044.

[80]  Umberto Triacca,et al.  Is Granger causality analysis appropriate to investigate the relationship between atmospheric concentration of carbon dioxide and global surface air temperature? , 2005 .

[81]  Weile Wang,et al.  The Relation between the North Atlantic Oscillation and SSTs in the North Atlantic Basin , 2004 .

[82]  Ramesh P. Singh,et al.  Further evidences for the weakening relationship of Indian rainfall and ENSO over India , 2004 .

[83]  G. Rangarajan,et al.  Analyzing multiple nonlinear time series with extended Granger causality , 2004, nlin/0405016.

[84]  H. V. D. Dool,et al.  Sensitivity of Teleconnection Patterns to the Sign of Their Primary Action Center , 2003 .

[85]  Elizabeth C. Kent,et al.  Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century , 2003 .

[86]  M. Kaminski,et al.  Determination of information flow direction among brain structures by a modified directed transfer function (dDTF) method , 2003, Journal of Neuroscience Methods.

[87]  Anders Moberg,et al.  Daily dataset of 20th‐century surface air temperature and precipitation series for the European Climate Assessment , 2002 .

[88]  G. Keller,et al.  Entropy of interval maps via permutations , 2002 .

[89]  B. Pompe,et al.  Permutation entropy: a natural complexity measure for time series. , 2002, Physical review letters.

[90]  S. Gyimah,et al.  Missing Data in Quantitative Social Research , 2001 .

[91]  Umberto Triacca,et al.  On the use of Granger causality to investigate the human influence on climate , 2001 .

[92]  S. Bressler,et al.  Evaluating causal relations in neural systems: Granger causality, directed transfer function and statistical assessment of significance , 2001, Biological Cybernetics.

[93]  Dimitrios Gyalistras,et al.  North Atlantic Oscillation – Concepts And Studies , 2001 .

[94]  Luiz A. Baccalá,et al.  Partial directed coherence: a new concept in neural structure determination , 2001, Biological Cybernetics.

[95]  M Palus,et al.  Synchronization as adjustment of information rates: detection from bivariate time series. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[96]  Suranjana Saha,et al.  Empirical Orthogonal Teleconnections , 2000 .

[97]  B. Goswami,et al.  Indian Monsoon–ENSO Relationship on Interdecadal Timescale , 2000 .

[98]  Schreiber,et al.  Measuring information transfer , 2000, Physical review letters.

[99]  H. Wanner,et al.  Reconstruction of monthly NAO and EU indices back to AD 1675 , 1999 .

[100]  Rajagopalan,et al.  On the weakening relationship between the indian monsoon and ENSO , 1999, Science.

[101]  J. Martinerie,et al.  Nonlinear analyses of interictal EEG map the brain interdependences in human focal epilepsy , 1999 .

[102]  A. Kulkarni,et al.  Rainfall variability over south-east Asia : Connections with Indian monsoon and enso extremes : New perspectives , 1997 .

[103]  R. Burke,et al.  Detecting dynamical interdependence and generalized synchrony through mutual prediction in a neural ensemble. , 1996, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[104]  E. Brook,et al.  Rapid Variations in Atmospheric Methane Concentration During the Past 110,000 Years , 1996, Science.

[105]  Jim Malley,et al.  A non-parametric approach to non-linear causality testing , 1996 .

[106]  Craig Hiemstra,et al.  Testing for Linear and Nonlinear Granger Causality in the Stock Price-Volume Relation , 1994 .

[107]  Joseph P. Romano,et al.  The stationary bootstrap , 1994 .

[108]  James Theiler,et al.  Testing for nonlinearity in time series: the method of surrogate data , 1992 .

[109]  A. Barnston,et al.  Classification, seasonality and persistence of low-frequency atmospheric circulation patterns , 1987 .

[110]  Will Tribbey,et al.  Numerical Recipes: The Art of Scientific Computing (3rd Edition) is written by William H. Press, Saul A. Teukolsky, William T. Vetterling, and Brian P. Flannery, and published by Cambridge University Press, © 2007, hardback, ISBN 978-0-521-88068-8, 1235 pp. , 1987, SOEN.

[111]  Fraser,et al.  Independent coordinates for strange attractors from mutual information. , 1986, Physical review. A, General physics.

[112]  K. Trenberth,et al.  The Northern Hemisphere Sea-Level Pressure Data Set: Trends, Errors and Discontinuities , 1980 .

[113]  C. Granger.  Investigating causal relations by econometric models and cross-spectral methods , 1969 .

[114]  J. William Ahwood,et al.  CLASSIFICATION , 1931, Foundations of Familiar Language.

[115]  J. van Huissteden,et al.  Methane , 2020, Thawing Permafrost.

[116]  G. Liger-Belair,et al.  Bubble dynamics in champagne and sparkling wines: Recent advances and future prospects , 2017 .

[117]  David I. Stern,et al.  Anthropogenic and natural causes of climate change , 2013, Climatic Change.

[118]  Susanne Rässler,et al.  Imputation , 2012 .

[119]  W. Marsden.  I and J , 2012 .

[120]  José Amigó,et al.  Permutation Complexity in Dynamical Systems , 2010 .

[121]  Nina F. Thornhill,et al.  Finding the Direction of Disturbance Propagation in a Chemical Process Using Transfer Entropy , 2007, IEEE Transactions on Control Systems Technology.

[122]  G. DiCesare.  Imputation, Estimation and Missing Data in Finance , 2006 .

[123]  Elizabeth C. Kent,et al.  Global analyses of sea surface temperature , 2003 .

[124]  H. Storch,et al.  Extending North Atlantic Oscillation reconstructions back to 1500 , 2001 .

[125]  John Geweke,et al.  Inference and causality in economic time series models , 1984 .

[126]  F. Takens.  Detecting strange attractors in turbulence , 1981 .