## A Handbook of Time-series Analysis, Signal Processing and Dynamics, Volume 1The Handbook of Time Series Analysis, Signal Processing and Dynamics serves as both as a text in time-series analysis and signal processing and a reference for research workers and practitioners. Time-series analysis and signal processing are two closely related subjects which are the concern of a wide range of applied disciplines, including statistics, econometrics, electrical engineering, mechanical engineering and physics. The first part of the Handbook covers the mathematical theory which is the foundation of the two subjects. It then moves onto an extensive treatment of the numerical analysis which, while specifically to the subjects of time-series analysis and signal processing, is of a much wider interest. The third part of the text covers time-series analysis and signal processing themselves. The Handbook also serves as an accessible work of reference. The computer code which implements the algorithms is woven into the text binding closely with the mathematical exposition. This allows the detailed workings of the algorithms to be understood quickly. The accompanying Macintosh/DOS hybrid CD Rom contains extensive libraries of computer programs and computer code together with |

### What people are saying - Write a review

We haven't found any reviews in the usual places.

### Contents

The Methods of TimeSeries Analysis | 3 |

Polynomial Methods | 21 |

Elements of Polynomial Algebra | 23 |

Rational Functions and Complex Analysis | 55 |

Polynomial Computations | 89 |

Difference Equations and Differential Equations | 121 |

Vector Difference Equations and StateSpace Models | 161 |

LeastSquares Methods | 179 |

Fourier Series and Fourier Integrals | 365 |

The Discrete Fourier Transform | 399 |

The Fast Fourier Transform | 427 |

TimeSeries Models | 457 |

Linear Filters | 459 |

Autoregressive and MovingAverage Processes | 513 |

TimeSeries Analysis in the Frequency Domain | 549 |

Prediction and Signal Extraction | 575 |

Matrix Computations | 181 |

Classical Regression Analysis | 201 |

Recursive LeastSquares Estimation | 227 |

Estimation of Polynomial Trends | 261 |

Smoothing with Cubic Splines | 293 |

Unconstrained Optimisation | 323 |

Fourier Methods | 363 |

### Other editions - View all

### Common terms and phrases

algorithm analogue analysis approximation autocovariance generating function autoregressive begin calculated chapter coefficients components computing condition consider convergence convolution corresponding cosine criterion function cubic decomposition defined derived described diagonal difference equation discrete Fourier transform discrete-time Fourier transform dispersion matrix elements estimate example expression factor factorisation Figure finite follows formula Fourier series Fourier transform frequency domain given gives Hessian matrix identity integer Intentionally Left Blank interval inverse iterations Kalman filter lag operator lambda least-squares linear lower-triangular lowpass filter method minimising moving-average process multiplication nonzero observations obtained operator orthogonal parameters periodic periodogram points prediction errors procedure quadratic quadratic function rational function real-valued recursive regression represented result roots sample sequence signal smoothing solution spectral density spectral density function spectrum spline stationary stationary process Statistical Tcap theorem time-series Toeplitz unit circle values varEpsilon variables variance vector white-noise process zero