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. |
Contents
ARMA Model and Model Fitting | 36 |
12 | 76 |
Digital Processing of a Dynamic Marine Gravity Meter | 113 |
18 | 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 | 157 |
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 |
The Water Flow Prediction in Xiang River | 235 |
Miscellaneous Cases Study | 250 |
| 273 | |
Component Analysis of Time Series in Frequency Domain | 162 |
Practical checking | 169 |
Periodicity Analysis of LH Release in Isolated Pituitary | 178 |
148 | 275 |
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Common terms and phrases
algorithm amplitudes AR(p ARMA model ARRSES Chapter Corollary correlation covariance function criterion defined Definition detecting distribution exists extreme value fitted model following theorem forecasting formula frequency domain Gaussian gravity meter H₂ Hilbert space introduced K₁ LH release listed in Table mathematical matrix model fitting normal observed data obtain occultation outliers output parameters polynomial populations prediction procedure R₁ random variables recursive rewrite ring signals rings of Uranus sample satisfies the condition seasonal component 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 theory and methods Uranus vector VEP records weak-P white noise Wold decomposition Xiang river Yule-Walker equation λε Π λεπ