## Case Studies in Time Series AnalysisThis book is a monograph on case studies using time series analysis, which includes the main research works applied to practical projects by the author in the past 15 years. The works cover different problems in broad fields, such as: engineering, labour protection, astronomy, physiology, endocrinology, oil development, etc. The first part of this book introduces some basic knowledge of time series analysis which is necessary for the reader to understand the methods and the theory used in the procedure for solving problems. The second part is the main part of this book ? case studies in different fields. |

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### Contents

ARMA Model and Model Fitting | 36 |

Prediction Filtering and Spectral Analysis of Time | 72 |

Digital Processing of a Dynamic Marine Gravity Meter | 113 |

Design a new digital filter under MinMax criterion | 120 |

The frequency rectification by filtering | 129 |

Digital Filters Design by Maximum Entropy Modelling | 135 |

A practical filter design | 144 |

Statistical analysis for detection of characteristics | 153 |

Practical rhythm analysis of LH release | 185 |

Statistical Detection of Uranian Ring Signals from | 193 |

Discussion | 204 |

A new model fitting procedure for freight transportation prediction | 212 |

Forecasting for freight transportation of practical data | 218 |

Appendix VII | 226 |

The Water Flow Prediction in Xiang River | 235 |

Constructing a prediction formula based on the hidden periodicities | 236 |

Appendix III | 159 |

Statistical Analysis of VEP and AI by the Principal | 162 |

Practical checking | 169 |

Periodicity Analysis of LH Release in Isolated Pituitary | 178 |

system | 261 |

273 | |

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### Common terms and phrases

amplitudes AR(p ARMA model ARRSES Chapter China Corollary covariance function defined Definition detecting distribution exists extreme value fitted model following theorem forecasting formula frequency domain Gaussian Group hidden frequencies Hilbert space introduced LH release linear model listed in Table mathematical matrix model fitting n-variate normal observed data obtain occultation OCFF order estimate order selection outliers output polynomial populations prediction principal component procedure random variables rats represented rewrite ring signals rings of Uranus sample satisfies the condition seasonal component second order series analysis shows spectral analysis spectral density Spectral estimates spectral function stationary process stationary series statistical Step stochastic integral stochastic process Suppose that x(t Theorem theory and methods trend component Uranus vector VEP records weak-P white noise wide sense Wold coefficients Wold decomposition X-ll Xiang river Yule-Walker equation