Nonlinear analysis of river flow time sequences
暂无分享,去创建一个
[1] Luca Ridolfi,et al. Clues to the existence of deterministic chaos in river flow , 1996 .
[2] Upmanu Lall,et al. Nonlinear dynamics and the Great Salt Lake: A predictable indicator of regional climate , 1996 .
[3] Giuseppe Rega,et al. An exploration of chaos , 1996 .
[4] Upmanu Lall,et al. Nonlinear Dynamics of the Great Salt Lake: Nonparametric Short-Term Forecasting , 1996 .
[5] Upmanu Lall,et al. Nonlinear Dynamics of the Great Salt Lake: Dimension Estimation , 1996 .
[6] A. Porporato,et al. ANALYSIS OF RANDOMLY SAMPLED DATA USING FUZZY AND NONLINEAR TECHNIQUES , 1996 .
[7] Upmanu Lall,et al. The Great Salt Lake: A Barometer of Low-Frequency Climatic Variability , 1995 .
[8] A. Mees,et al. On selecting models for nonlinear time series , 1995 .
[9] A. Rinaldo,et al. Can One Gauge the Shape of a Basin , 1995 .
[10] S. Peckham. New Results for Self‐Similar Trees with Applications to River Networks , 1995 .
[11] V. F. Pisarenko,et al. Statistical estimation of the correlation dimension , 1995 .
[12] M. Paluš. Testing for nonlinearity using redundancies: quantitative and qualitative aspects , 1994, comp-gas/9406002.
[13] J. Theiler,et al. Generalized redundancies for time series analysis , 1994, comp-gas/9405006.
[14] P. Claps,et al. Conceptually-based univariate stochastic modelling of river runoff , 1995 .
[15] Mike E. Davies,et al. Noise reduction schemes for chaotic time series , 1994 .
[16] F. Busse. An exploration of chaos: J. Argyris, G. Faust and M. Haase, Elsevier, Amsterdam, 1994, 722 pp., ISBN 0-444-82002-7 (hardbound), 0-444-82003-5 (paperback) , 1994 .
[17] Milan Paluš,et al. Testing for Nonlinearity in Weather Records , 1994 .
[18] R. Vio,et al. Luminosity Variations of 3C 345: Is There Any Evidence of Low-dimensional Chaos? , 1994 .
[19] Anastasios A. Tsonis,et al. Searching for determinism in observed data: a review of the issues involved , 1994 .
[20] K. Judd. Estimating dimension from small samples , 1994 .
[21] L. Tsimring,et al. The analysis of observed chaotic data in physical systems , 1993 .
[22] G. Kember,et al. Forecasting river flow using nonlinear dynamics , 1993 .
[23] Daw,et al. Role of low-pass filtering in the process of attractor reconstruction from experimental chaotic time series. , 1993, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.
[24] Schreiber,et al. Extremely simple nonlinear noise-reduction method. , 1993, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.
[25] P. Grassberger,et al. On noise reduction methods for chaotic data. , 1993, Chaos.
[26] Takayasu,et al. Water erosion as a fractal growth process. , 1993, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.
[27] Eric R. Ziegel,et al. Developments in Time Series Analysis , 1993 .
[28] Gerd Pfister,et al. Comparison of algorithms calculating optimal embedding parameters for delay time coordinates , 1992 .
[29] J. Maquet,et al. Construction of phenomenological models from numerical scalar time series , 1992 .
[30] Leonard A. Smith,et al. Distinguishing between low-dimensional dynamics and randomness in measured time series , 1992 .
[31] T. Sauer. A noise reduction method for signals from nonlinear systems , 1992 .
[32] Julien Clinton Sprott,et al. Extraction of dynamical equations from chaotic data , 1992 .
[33] J. Elsner,et al. Nonlinear prediction as a way of distinguishing chaos from random fractal sequences , 1992, Nature.
[34] Guan-Hsong Hsu,et al. SNR perfomance of a noise reduction algorithm applied to coarsely sampled chaotic data , 1992 .
[35] James B. Elsner,et al. Predicting time series using a neural network as a method of distinguishing chaos from noise , 1992 .
[36] W. Ditto,et al. Chaos: From Theory to Applications , 1992 .
[37] Anastasios A. Tsonis,et al. Nonlinear Prediction, Chaos, and Noise. , 1992 .
[38] M. Casdagli. Chaos and Deterministic Versus Stochastic Non‐Linear Modelling , 1992 .
[39] James Theiler,et al. Testing for nonlinearity in time series: the method of surrogate data , 1992 .
[40] P. Grassberger,et al. A simple noise-reduction method for real data , 1991 .
[41] P. Grassberger,et al. NONLINEAR TIME SEQUENCE ANALYSIS , 1991 .
[42] E. Lorenz. Dimension of weather and climate attractors , 1991, Nature.
[43] J. Theiler. Some Comments on the Correlation Dimension of 1/fαNoise , 1991 .
[44] K. Pawelzik,et al. Optimal Embeddings of Chaotic Attractors from Topological Considerations , 1991 .
[45] P. Linsay. An efficient method of forecasting chaotic time series using linear interpolation , 1991 .
[46] M. Mundt,et al. Chaos in the sunspot cycle: Analysis and Prediction , 1991 .
[47] J. D. Farmer,et al. Optimal shadowing and noise reduction , 1991 .
[48] J. D. Farmer,et al. A Theory of State Space Reconstruction in the Presence of Noise , 1991 .
[49] Breeden,et al. Reconstructing equations of motion from experimental data with unobserved variables. , 1990, Physical review. A, Atomic, molecular, and optical physics.
[50] Grebogi,et al. Shadowing of physical trajectories in chaotic dynamics: Containment and refinement. , 1990, Physical review letters.
[51] Stephen M. Hammel,et al. A noise reduction method for chaotic systems , 1990 .
[52] P. Grassberger. An optimized box-assisted algorithm for fractal dimensions , 1990 .
[53] George Sugihara,et al. Nonlinear forecasting as a way of distinguishing chaos from measurement error in time series , 1990, Nature.
[54] Pfister,et al. Attractor reconstruction from filtered chaotic time series. , 1990, Physical review. A, Atomic, molecular, and optical physics.
[55] Konstantine P. Georgakakos,et al. Evidence of Deterministic Chaos in the Pulse of Storm Rainfall. , 1990 .
[56] James A. Yorke,et al. Noise Reduction: Finding the Simplest Dynamical System Consistent with the Data , 1989 .
[57] Ranvir Singh,et al. Watershed Planning and Analysis in Action , 1990 .
[58] N. F. Jr. Hunter,et al. Application of nonlinear time series models to driven systems , 1990 .
[59] H. Schuster,et al. Proper choice of the time delay for the analysis of chaotic time series , 1989 .
[60] Peter Grassberger,et al. Information content and predictability of lumped and distributed dynamical systems , 1989 .
[61] Konstantine P. Georgakakos,et al. Chaos in rainfall , 1989 .
[62] Martin Casdagli,et al. Nonlinear prediction of chaotic time series , 1989 .
[63] A. Provenzale,et al. Finite correlation dimension for stochastic systems with power-law spectra , 1989 .
[64] J. Havstad,et al. Attractor dimension of nonstationary dynamical systems from small data sets. , 1989, Physical review. A, General physics.
[65] J. Doyne Farmer,et al. Exploiting Chaos to Predict the Future and Reduce Noise , 1989 .
[66] Agnessa Babloyantz,et al. A comparative study of the experimental quantification of deterministic chaos , 1988 .
[67] Yorke,et al. Noise reduction in dynamical systems. , 1988, Physical review. A, General physics.
[68] J. Elsner,et al. The weather attractor over very short timescales , 1988, Nature.
[69] Broggi,et al. Dimension increase in filtered chaotic signals. , 1988, Physical review letters.
[70] S. Yakowitz,et al. Rainfall-runoff forecasting methods, old and new , 1987 .
[71] Theiler,et al. Efficient algorithm for estimating the correlation dimension from a set of discrete points. , 1987, Physical review. A, General physics.
[72] Farmer,et al. Predicting chaotic time series. , 1987, Physical review letters.
[73] 秦 浩起,et al. Characterization of Strange Attractor (カオスとその周辺(基研長期研究会報告)) , 1987 .
[74] S. Yakowitz,et al. Nearest‐neighbor methods for nonparametric rainfall‐runoff forecasting , 1987 .
[75] Swinney,et al. Strange attractors in weakly turbulent Couette-Taylor flow. , 1987, Physical review. A, General physics.
[76] James P. Crutchfield,et al. Equations of Motion from a Data Series , 1987, Complex Syst..
[77] Gilbert Strang,et al. Introduction to applied mathematics , 1988 .
[78] A. Provenzale,et al. A search for chaotic behavior in large and mesoscale motions in the Pacific Ocean , 1986 .
[79] Theiler,et al. Spurious dimension from correlation algorithms applied to limited time-series data. , 1986, Physical review. A, General physics.
[80] Fraser,et al. Independent coordinates for strange attractors from mutual information. , 1986, Physical review. A, General physics.
[81] G. Nicolis,et al. Is there a climatic attractor? , 1984, Nature.
[82] James P. Crutchfield,et al. Low-dimensional chaos in a hydrodynamic system , 1983 .
[83] P. Grassberger,et al. Measuring the Strangeness of Strange Attractors , 1983 .
[84] P. Grassberger,et al. Characterization of Strange Attractors , 1983 .
[85] F. Takens. Detecting strange attractors in turbulence , 1981 .
[86] Peter K. Kitanidis,et al. Real‐time forecasting with a conceptual hydrologic model: 1. Analysis of uncertainty , 1980 .
[87] James P. Crutchfield,et al. Geometry from a Time Series , 1980 .
[88] H. Tong,et al. Threshold Autoregression, Limit Cycles and Cyclical Data , 1980 .
[89] M. Priestley. STATE‐DEPENDENT MODELS: A GENERAL APPROACH TO NON‐LINEAR TIME SERIES ANALYSIS , 1980 .
[90] D. H. Pilgrim. Travel Times and Nonlinearity of Flood Runoff From Tracer Measurements on a Small Watershed , 1976 .
[91] E. Lorenz. Atmospheric Predictability as Revealed by Naturally Occurring Analogues , 1969 .