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INDEX
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Abarbanel, H. D. I. (1995). Analysis of observed chaotic data. New York: Springer-Verlag.
Abramowitz, M., & Stegun, I. A. (Eds.). (1972). Handbook of mathematical functions with formulas, graphs, and mathematical tables (9th printing). New York: Dover.
Alligood, K. T., Sauer, T. D., & Yorke, J. A. (1996). Chaos: An introduction to dynamical systems. New York: Springer.
Ash, C. (1992). The probability tutoring book: An intuitive course for engineers and scientists (revised printing). New York: IEEE.
Babovic, V., Keijzer, M., & Stefansson, M. (2001). Chaos Theory, Optimal Embedding and Evolutionary Algorithms. Retrieved January, 2004, from citeseer.ist.psu.edu/babovic01chaos.shtml.
Baker, L. (1991). C mathematical function handbook. New York: McGraw-Hill.
Berry, M. W., & Minser, K. S. (1999). Algorithm 798: High-dimensional interpolation using the modified Shepard method. ACM Trans. on Mathematical Software, 25 (3), 353-366.
Berthold, M., and Hand, D. (Eds.). Intelligent data analysis: An introduction. New York: Springer.
Blum, A. (1992). Neural networks in C++: An object-oriented framework for building connectionist systems. New York: John Wiley & Sons.
Boffetta, G., Crisanti, A., Paparella, F., Provenzale, A., Vulpiani, A. (1998). Slow and fast dynamics in coupled systems: A time series analysis view; Physica D, 116, 301-312.
Boker, S. M. (1996). Linear and nonlinear dynamical systems data analytic techniques and an application to developmental data. Unpublished doctoral dissertation, University of Virginia.
Box, G. E. P., & Pierce, D. A. (1970). Distribution of residual autocorrelations in autoregressive-integrated moving average time series models. J. Amer. Statist. Assoc., 65, 1509-1526.
Bradley, E. (1999). Time series analysis. In Berthold, M., and Hand, D. (Eds.). Intelligent data analysis: An introduction. (pp. 167-194). New York: Springer.
Braha, D. (2001). Data mining for design and manufacturing. Norwell, MA: Kluwer Academic Publishers.
Brockwell, P. J., & Davis, R. A. (1996). Introduction to time series and forecasting. New York: Springer.
Broer, H. W., Krauskopf, B., & Vegter, G. (Eds). (2001). Global analysis of dynamical systems. London, England: Institute of Physics Publishing.
Bryant, V. (1990). Yet another introduction to analysis. Cambridge, England: Cambridge University Press.
Budd, T. A. (1994). Classic data structures in C++ (reprint). Reading, MA: Addison-Wesley.
Casdagli, M., Iasemidis, L., Sackellares, J., Roper, S., Gilmore, & Savit, R. (1996). Characterizing nonlinearity in invasive EEG recordings from temporal lobe epilepsy. Physica D, 99 (2/3), 381-399.
Chapra, S. C., & Canale, R. P. (2002). Numerical methods for engineers: With software and programming applications (4th ed.). Boston: McGraw-Hill.
Drazin, P. G. (1992). Nonlinear systems. Cambridge, England: Cambridge University Press.
Devaney, R. L. (1992). A first course in chaotic dynamical systems: Theory and experiment. Reading, MA: Addison-Wesley.
Eckmann, J.-P., Kamphorst, S. O., & Ruelle, D. (1987). Recurrence plots of dynamical systems. Europhysics Letters (EPL), 4(9), 973-977. https://doi.org/10.1209/0295-5075/4/9/004
Ellner, S. P. (1988). Estimating attractor dimensions from small data sets. Physica D: Nonlinear Phenomena, 32(1), 132-144. https://doi.org/10.1016/0167-2789(88)90088-3
Frank, R. J., Davey, N., & Hunt, S .P. (2001). Time series prediction and neural networks. Journal of Intelligent and Robotic Systems, 31, 91-103.
Franke, J., Haerdle, W., & Hafner, C. (2004). Introduction to statistics of financial markets. Retrieved March, 2005, from quantlet.com/mdstat/scripts/sfe/pdf/sfe.pdf.
Fraser, A. M., & Swinney, H. (1986). Independent coordinates for strange attractors from mutual information. Phys. Rev. A, 33, 1134–1140.
Fujimoto, Y., & Iokibe, T. (2000). Evaluation of deterministic property of time series by the method of surrogate data and the trajectory parallel measure method. IEICE Trans. Fundamentals, E83-A (2), 343-349.
Gentle, J. E., Haerdle, W., Mori, Y. (Eds.). (2004). Handbook of computational statistics. Berlin, Germany: Springer.
Gikhman, I. I., & Skorokhod, A. V. (1996). Introduction to theory of random processes (Scripta Technica, Trans.). New York: Dover.
Gilmore, R. (1981). Catastrophe theory for scientists and engineers. New York: Dover.
Gilmore, R. (1998). Topological analysis of chaotic dynamical systems. Rev. Mod. Phys., 70 (4), 1455-1530.
Gonzalez, R. C., & Woods, R. E. (1992). Digital image processing. Reading, MA: Addison-Wesley.
Grassberger, P. (1983). Generalized dimensions of strange attractors. Phys. Lett., A. 97 (6), 227-230.
Grassberger, P. (1985). Generalizations of the Hausdorff dimension of fractal measures. Phys. Lett. A, 107, 101105.
Grassberger, P. (1990). An optimal box-assisted algorithm for fractal dimensions. Phys. Lett. A, 148, 63-68.
Grassberger, P., & Procaccia, I. (1983). Characterization of strange attractors. Phys. Rev. Lett., 50 (5), 346-349.
Grassberger, P., & Procaccia, I. (1983). Measuring the strangeness of strange attractors. Physica D, 9, 189-208.
Grassberger, P., Schreiber, T., & Schaffrath, C. (1991). Nonlinear time sequence analysis. Int J. Bifurc. Chaos, 1, 521-547.
Grosse, E. (1988). A Catalog Of Algorithms For Approximation. Retrieved August, 2003, from http://citeseer.ist.psu.edu/12579.shtml.
Gullberg, J. (1997). Mathematics: From the birth of numbers. New York: W. W. Norton & Company Ltd.
Haerdle, W. (1990). Applied nonparametric regression. Cambridge, England: Cambridge University Press.
Haerdle, W., Mueller, M., Sperlich, S., & Werwatz, A. (2004). Nonparametric and semiparametric models. Heidelberg, Germany: Springer.
Haerdle, W., & Simar, L. (2003). Applied multivariate statistical analysis. Berlin, Germany: Springer.
Hanke, J. E., & Reitsch, A.G. (1994). Understanding business statistics (2nd ed.). Burr Ridge, IL: Irwin.
Harvill, J. & Ray, B. (2000). Lag identification for vector nonlinear time series. Communications in Statistic, Theory and Methods, 29 (8), 1677-1702.
Hlavka, Z. (2000). Robust sequential methods. Berlin, Germany. Retrieved March, 2005, from quantlet.com/mdstat/scripts/rsm/rsmpdf.pdf.
Iasemidis, L., & Sackellares, C. (1996). Chaos theory and epilepsy. The Neuroscientist, 2, 118-126.
Ichige K., Iwaki M., & Ishii R. (2000). Accurate estimation of minimum filter length for optimum FIR digital filters. Circuits and Systems II: Analog and Digital Signal Processing, IEEE Transactions. 47 (10).
Ifeachor, E. C., & Jervis, B. W. (1996). Digital signal processing: A practical approach. Reading, MA: Addison-Wesley.
Inmon, W. H., & Bird, Thomas J. Jr. (1986). The dynamics of database. Englewood Cliffs, NJ: Prentice-Hall.
Iwanski, J. S., & Bradley, E. (1998). Recurrence plots of experimental data: To embed or not to embed? Chaos, 8 (4), 861871.
James, F. (1990). A review of pseudorandom number generators. Computer Phys. Comm., 60 (4), 329-344.
Kantz, H. (1994). A robust method to estimate the maximal Lyapunov exponent of a time series. Phys. Lett. A, 185, 77-87.
Kantz, H., & Schürmann, T. (1996). Enlarged scaling ranges for the KS-entropy and the information dimension. Chaos, 6, 167-171.
Kantz, H., & Schreiber, T. (1997). Nonlinear time series analysis (reprint). Cambridge, England: Cambridge University Press.
Kendall, M. G., & Stuart, A. (1976). The advanced theory of statistics, Vol. 3. Griffin, London.
Kennedy, R. L., Lee, Y., Van Roy, B., Reed, Christopher D., & Lippmann, R. P. (1998). Solving data mining problems through pattern recognition. Upper Saddle River, NJ: Prentice Hall.
Kennel, M. B., Brown, R., & Abarbanel, H. D. I. (1992). Determining embedding dimension for phase-space reconstruction using a geometrical construction. Phys. Rev. A. 45, 3403-3411.
Kennel, M. B. (1997). Statistical test for dynamical nonstationarity in observed time series data. Phys. Rev. E, 56 (1), 316321.
Kennel, M. B., & Abarbanel, H. D. I. (2002). False neighbors and false strands: A reliable minimum embedding dimension algorithm. Phys. Rev. E, 66, 026209, 1-19.
Kennel, M. B., Shlens, J., Abarbanel, H. D. I., & Chichilnisky, E. J. (2005). Estimating entropy rates with Bayesian confidence intervals. Neural Computation, 17 (7), 1531-1576.
Kincaid, D. R., & Cheney, E. W. (1990). Numerical analysis: Mathematics of science of computing. Pacific Grove, CA: Brooks/Cole.
Kruse, R. L. (1984). Data structures and program design. Englewood Cliffs, NJ: Prentice-Hall.
Kugiumtzis, D., Lillekjendlie, B., & Christophersen, N. (1994). Chaotic time series I. Modeling, Identification and Control, 15 (4), 205-224.
Kugiumtzis, D. (1997). Correction of the correlation dimension for noisy time series, Int. J. Bif. Chaos, 7 (6), 1283-1294.
Kugiumtzis, D., & Christophersen, N. (1997). State space reconstruction: Method of delays vs singular spectrum approach (Research Rep. No. 236). Oslo, Norway: University of Oslo.
Kugiumtzis, D., Lingjaerde N., & Christophersen, N. (1998). Regularized local linear prediction of chaotic time series. Physica D, 112, 344360.
Kugiumtzis, D. (2001). On the reliability of the surrogate data test for nonlinearity in the analysis of noisy time series. Int. J. Bifurcation and Chaos, 11 (7), 1881-1896.
Lai, Y. -C., & Lerner, D. (1998). Effective scaling regime for computing the correlation dimension from chaotic time series. Physica D, 115, 118.
Lebedev, N. N. (1972). Special functions & their applications (Richard A. Silverman, Trans.). New York: Dover.
Lehnertz, K. (1999). Non-linear time series analysis of intracranial EEG recordings in patients with epilepsy-an overview. Int. J. Psychophysiol, 34 (1), 45-52.
Lillekjendlie, B., Kugiumtzis, D., & Christophersen, N. (1994). Chaotic time series part II: System identification and prediction. Modeling, Identification and Control, 15 (4), 225243.
Ljung, G. M., & Box, G. E. P. (1978). On a measure of lack of fit in time series models. Biometrika, 65, 297-303.
Mandel, J. (1964). The statistical analysis of experimental data. New York: Dover.
Manetti, C., Ceruso, M.-A., Giuliani, A., Webber, C.L., Jr., Zbilut, J.P. (1999). Recurrence quantification analysis as a tool for the characterization of molecular dynamics sumilations. Phys. Rev. E, 59, 992-998.
Manna, Z., & Waldinger, R. (1985). The logical basis for computer programming (vol. 1). Reading, MA: Addison-Wesley.
Marsaglia, G., Zaman A., & Tsang, W. W. (1987). Toward a universal random number generator (Tech. Rep. No. FSU-SCRI-87-50). Miami: Florida State University.
McLeod, A. I., & Li, W. K. (1983). Diagnostic checking ARMA time series models using squared-residual autocorrelations. J. Time Series Anal., 4, 269-273.
Masters, T. (1993). Advanced Algorithms for neural networks: A C++ sourcebook. New York: John Wiley & Sons.
Masters, T. (1993). Practical neural network recipes in C++. New York: John Wiley & Sons.
Masters, T. (1994). Signal and image processing with neural networks: A C++ Sourcebook. New York: John Wiley & Sons.
Masters, T. (1995). Neural, novel & hybrid algorithms for time series prediction. New York: John Wiley & Sons.
McNaughton, R. (1982). Elementary computability, formal languages and automata. Englewood Cliffs, NJ: Prentice-Hall.
Meijering, E. (2002). A chronology of interpolation: From ancient astronomy to modern signal and image processing. Proceedings of the IEEE, 90 (3), 319-342.
Merrit, J. (2002). Using Fourier interpolation to find C∞ approximations of a sampled function and its derivatives. Retrieved June, 2003, from warpax.com/articles/fourier-interpolation/fourier-interpolation.pdf.
Meyers, S. (1992). Effective C++. Reading, MA: Addison-Wesley.
Miller, S. (1994). Experimental design and statistics (2nd. ed. reprint). New York: Routledge.
Mueller, M. (2000). Semiparametric extensions to generalized linear models. Berlin, Germany. Retrieved March, 2005, from marlenemueller.de/publications/HabilMM.pdf
Naidu, P. S. (1996). Modern spectrum analysis of time series. Boca Raton, FL: CRC Press.
Nair, A., Jyh-Charn, L., Rilett, L., & Gupta, S. (2001). Non-linear analysis of traffic flow. Proceedings of the 2001 IEEE on Intelligent Transportation Systems. Proceedings of the IEEE, Oakland, CA, Aug, 25-29, 2001, (pp. 681-685). Piscataway, NJ: Institute of Electrical and Electronic Engineers.
Nicolis, G. (1995). Introduction to nonlinear science. Cambridge, England: Cambridge University Press.
Otani M., & Jones A. J. (1997). Guiding chaotic orbits (Tech. Rep.). Cardiff, Wales: Department of Computing, Imperial College of Science, Technology and Medicine.
Ott, E., Sauer, T., & Yorke, J. A. (Eds.). (1994). Coping with chaos: Analysis of chaotic data and the exploration of chaotic systems. New York: John Wiley & Sons.
Palus, M. (1996). Coarse-grained entropy rates for characterization of complex time series. Physica D, 93, 64-77.
Palus, M. & Hoyer, D. (1998). Surrogate data in detecting nonlinearity and phase synchronization. IEEE Engineering and Medicine and Biology, 17, 40-45.
Pasternack, G. B. (1999). Does the river run wild? Assessing chaos in hydrological systems. Advs.in Water Resour., 23, 253-260.
Pohl, I. (1993). Object-oriented programming using C++. Redwood City, CA: Benjamin/Cummings.
Pompe, B., & Heilfort, M. (1995). On the concept of the generalized mutual information function and efficient algorithms for calculating it. Retrieved October, 2002, from www2.physik.uni-greifswald.de/~pompe/p-papers.htm#2.
Pratt, W. K. (1991). Digital image processing (2nd ed.). New York: John Wiley & Sons.
Prichard, D., & Price, C. P. (1992). Spurious dimension estimates from time series of geomagnetic indices. Geophys. Res. Lett., 19 (15), 16231626.
Pritchard, J. (1992). The chaos cookbook: A practical programming guide (2nd ed.). Oxford, England: Butterworth-Heinemann.
Provenzale, A., Smith, L. A., Vio, R., & Murante, G. (1992). Distinguishing between low-dimensional dynamics
and randomness in measured time series. Physica D, 58, 31-49.
Raidl, A. (1996). Estimating the Fractal Dimension, k-2 entropy and predictability of the atmosphere. Czech. J. Phys., 46 (4). 293-328.
Rao, V. B., & Rao, H. V. (1995). C++ neural networks & fuzzy logic (2nd ed.). New York: MIS:Press.
Rivlin, T. J. (1981). An introduction to the approximation of functions (revised printing). New York: Dover.
Renka, R. J. (1988). Algorithm 660: QSHEP2D: Quadratic Shepard method for bivariate interpolation of scattered data. ACM Trans. Math. Softw. 14 (2), 149150.
Renka, R. J. (1988a). Multivariate interpolation of large sets of scattered data. ACM Trans. Math. Softw. 14 (2), 139148.
Renka, R. J. (1999), Algorithm 790: CSHEP2D. Cubic Shepard method for bivariate interpolation of scattered data, ACM Trans. on Mathematical Software, 25 (1), 70-77.
Renyi, A., (1971). Probability Theory. Amsterdam: North Holland.
Rogers, J. (1997). Object oriented neural networks in C++. New York: Academic Press.
Rorabaugh, C. B. (1998). DSP primer. New York: McGraw-Hill.
Rosenstein, M. T., Collins, J. J., & De Luca, C. J. (1993). A practical method for calculating largest Lyapunov exponents from small data sets. Physica D, 65, 117-134.
Rosenstein, M. T., Collins, J. J., & De Luca, C. J. (1994). Reconstruction expansion as a geometry-based framework for choosing proper delay times, Physica D, 73, 82-98.
Savitzky, A., & Golay, M. J. M. (1964). Smoothing and differentiation of data by simplified least squares procedures. Anal. Chem., 36 (8), 1627-1639.
Schrieber T. (1999). Interdisciplinary application of non-linear time series methods. Phys. Rep., 308 (1), 1–86.
Schreiber, T. (2000). Measuring information transfer. Phys. Rev. Lett., 85, 461-464.
Schreiber, T., & Schmitz, A. (1996). Constrained randomization of time series for hypothesis testing. Retrieved June, 2005, from citeseer.ist.psu.edu/schreiber98constrained.shtml.
Schreiber, T., & Schmitz, A. (1996a). Improved surrogate data for nonlinearity tests. Phys. Rev. Lett., 77 (4), 635-638.
Schreiber, T., & Schmitz, A. (1997). On the discrimination power of measures for nonlinearity in a time series. Phys. Rev. E, 55, 5443-5447.
Schreiber, T., & Schmitz, A. (2000). Surrogate time series. Physica D, 142 (3-4), 346-382.
Sedgewick, R. (1992). Algorithms in C++. Reading, MA: Addison-Wesley.
Senn, J. A. (1984). Analysis and design of information systems. New York: McGraw-Hill.
Sethi, I. (2001). Data mining: An introduction. In D. Braha (Ed.). Data mining for design and manufacturing. Norwell, MA: Kluwer Academic Publishers.
Shannon, C. E. (1948). A Mathematical theory of communication. Retrieved April, 2005, from cm.bell-labs.com/cm/ms/what/shannonday/paper.shtml.
Shepard, D. (1968). A two-dimensional interpolation function for irregularly-spaced data. Proceedings, 1968 ACM National Conference, 517-523.
Simon, J. L. (1997). Resampling: The new statistics (2nd edition). Arlington, VA. Retrieved March, 2001, from resample.com/content/text/index.shtml.
Sivakumar, B. (2000). Chaos theory in hydrology: important issues and interpretations. Journal of Hydrology, 227, 120.
Small M., & Judd, K. (1998). Comparisons of new nonlinear modeling techniques with applications to infant respiration. Physica D, 117, 283-298.
Small, M., & Judd, K. (1999). Towards long-term prediction. Physica D, 136, 31-44.
Smith, S. W. (1998). The scientist and engineer's guide to digital signal processing. Retrieved June, 2005, from dspguide.com
Snedecor, G. W., & Cochran, W.G. (1989). Statistical methods (8th ed.). Ames, IA: Blackwell.
Skvarcius, R., & Robinson, W. B. (1986). Discrete mathematics with computer science applications. Menlo Park, CA: Benjamin/Cummings.
Sprott, J. C. (2000). Strange attractors: Creating patterns in chaos. Retrieved June, 2005, from sprott.physics.wisc.edu/fractals/booktext/sabook.pdf. (Original work published 1994).
Stamper, D. A. (1986). Business data communications. Menlo Park, CA: Benjamin/Cummings.
Stoer, J., & Burlirsch, R. (2002). Introduction to numerical analysis (3rd ed.) (R. Bartels, W. Gautschi, & C. Witzgall, Trans.). New York: Springer-Verlag.
Stroustrup, B. (1991). The C++ programming language (2nd ed.). Reading, MA: Addison-Wesley.
Strozzi, F., Zaldivar, J.-M., & Zbilut, J.P. (2002). Application of non-linear time series analysis techniques to high frequency currency exchange data. Physica A, 312, 520538.
Sveshnikov, A. A. (Ed.). (1978). Problems in probability theory, mathematical statistics and theory of random numbers. New York: Dover.
Swokowski, E. W. (1979). Calculus with analytic geometry (2nd ed.). Boston: Prindle, Weber & Schmidt.
Taylor, P. D. (1992). Calculus: The analysis of functions. Toronto, Canada: Wall & Emerson.
Theiler, J. (1986). Spurious dimension from correlation algorithms applied to limited time-series data. Phys. Rev. A, 34, 2427-2433.
Theiler, J. (1987). Efficient algorithm for estimating the correlation dimension from a set of discrete points. Phys. Rev. A, 36, 4456-4462.
Theiler, J. (1988). Lacunarity in a best estimator of fractal dimension. Phys. Lett. A, 135, 195-200.
Theiler, J. (1990). Statistical precision in dimension estimators. Phys. Rev. A, 41, 30381071.
Theiler, J. (1990a). Estimating fractal dimension. J. Opt. Soc. Am. A, 7 (6), 10551071.
Theiler, J., & Eubank, S. (1993). Don't Bleach Chaotic Data. Chaos, 3, 771-782.
Theiler, J., Eubank, S., Longtin, A., Galdrikian, B., Farmer, J. D. (1994). Testing for nonlinearity in time series: The method of surrogate data. Physica D, 58, 7794.
Theiler, J., & Lookman, T. (1993). Statistical error in a chord estimator of correlation dimension: The "rule of five". Int. J. Bif. Chaos, 3, 765-771.
Theiler, J., & Prichard, D. (1996). Constrained-realization Monte-Carlo method for hypothesis testing. Physica D, 94 (4), 221-235.
Tolstov, G. P. (1962). Fourier series (Richard A. Silverman, Trans.). New York: Dover.
Webber, C. L., Jr., & Zbilut, J. P. (1994). Dynamical assessment of physiological systems and states using recurrence plot strategies. J. Appl. Physiol., 76, 965-973.
Weigend, A. S., & Gershenfeld, N. A. (1994). The future of time series: Learning and understanding. In Weigend, A. S., and Gershenfeld, N. A. (Eds.). Time series prediction: Forecasting the future and understanding the past. (pp. 1-70). Reading, MA, Addison-Wesley.
Weiss, N. A. (2004). Introductory statistics (7th edition). New York: Pearson/Addison-Wesley.
Weiss, S. M., & Indurkhya, Nitin. (1998). Predictive data mining: A practical guide. San Francisco: Morgan Kaufmann.
Welstead, S. T. (1994). Neural networks and fuzzy logic applications in C/C++. New York: John Wiley & Sons.
Werner H., & Schaback R. (1979). Praktische mathematik II (2nd ed.). New York: Springer-Verlag.
Windhorst, U., & Johansson, H. (Eds.). Modern techniques in neuroscience research. Berlin, Germany: Springer-Verlag.
Yamamoto, Y. (1999). Detection of chaos and fractals from experimental time series. In Windhorst, U., & Johansson, H. (Eds.). Modern Techniques in Neuroscience Research. (pp. 669-687). Berlin, Germany: Springer-Verlag.
Yu, D., Lu, W., & Harrison, R.G. (1998). Space time index plots for probing dynamical nonstationarity. Phys. Lett. A, 250, 323-327
Yu, D., Lu, W., & Harrison, R. G. (1999). Detecting dynamical nonstationarity in time series data. Chaos, 9 (4), 865-870.
Zbilut, J.P., Giuliani, A., & Webber, C.L., Jr. (1998). Recurrence quantification analysis and principle components in the detection of short complex signals. Phys. Lett. A, 237, 131-135.