The practice of time series analysis
著者
書誌事項
The practice of time series analysis
（Statistics for engineering and physical science）
Springer, c1999
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注記
Includes bibliographies and index
内容説明・目次
内容説明
A collection of applied papers on time series, appearing here for the first time in English. The applications are primarily found in engineering and the physical sciences.
目次
1 Control of Boilers for Thermoelectric Power Plants by Means of a Statistical Model.- 1.1 Introduction.- 1.2 Problems in Controlling Multi variable System.- 1.3 System Analysis and Control by Means of Statistical Model.- 1.4 Practical Procedure for Optimal Controller Design.- 1.5 Application Results at Actual Plants.- 1.6 Closing Remarks.- 2 Feedback Analysis of a Living Body by a Multivariate Autoregressive Model.- 2.1 Introduction.- 2.2 Body Liquid Control and Feedback.- 2.3 Example of the Relative Power Contribution and Impulse Response.- 2.4 Using an Autoregressive Model for Feedback Analysis.- 2.5 Obtaining the Power Contribution.- 2.6 State Equation and Impulse Response.- 2.7 Impulse Response of the Closed and Open Systems.- 2.8 Confirmation by a Virtual Feedback System.- 2.9 Conclusions.- 3 Factor Decomposition of Economic Time Series Fluctuations - Economic and Statistical Models in Harmony.- 3.1 Introduction.- 3.2 Model 1 (Model with Stochastic Components Only).- 3.3 Model 2 (the Model Including Deterministic Components).- 3.4 Model 3 (the Model by Which Macroeconomic Policy Effect can Also be Measured).- 3.5 Has Fine Tuning been Successful?.- 3.6 Is Prediction Ability Available?.- 3.7 Conclusions and Subjects in the Future.- 4 The Statistical Optimum Control of Ship Motion and a Marine Main Engine.- 4.1 Introduction.- 4.2 Outline of the Control of the Motion of the Hull and Main Engine.- 4.3 Statistical Model of Ship Motions and Its Control.- 4.4 Design of Optimum Autopilot System Based on the Control-Type Autoregressive Model.- 4.5 Noise-Adaptive Control System.- 4.6 Rudder-Roll Control System.- 4.7 Application to the Marine Main Engine Governor System.- 4.8 Conclusions.- 5 High Precision Estimation of Seismic Wave Arrival Times.- 5.1 Introduction.- 5.2 Locally Stationary AR Model.- 5.3 Automatic Division of a Locally Stationary Interval.- 5.4 Precision Estimation of Seismic Wave Arrival Times.- 5.5 Application: Earthquake Location and Velocity Structure Determination from Precise Arrival Time Estimates.- 5.6 Conclusions.- 6 Analysis of Dynamic Characteristics of a Driver-Vehicle System.- 6.1 Introduction.- 6.2 Dynamics of Automobile under Later al-Wind Disturbance.- 6.3 Application of Multivariate AR Model to Driver-Vehicle System.- 6.4 Dynamics of Driver-Vehicle System under Lateral-Wind.- 6.5 Conclusions.- 7 Estimation of Directional Wave Spectra Using Ship Motion Data.- 7.1 Introduction.- 7.2 Cross-spectrum Analysis by a Multivariate AR Model.- 7.3 Relation Between the Directional Wave Spectrum and the Ship Motions.- 7.4 Estimation of the Directional Wave Spectrum Using a Bayesian Model.- 7.5 Results of the Tank Test Using a Model Ship.- 7.6 Conclusions.- 8 Control of Filature Production Process.- 8.1 Dropping-end Control and Gap Process.- 8.2 Size Control of Raw Silk.- 8.3 Dwell Time in a Black Box.- 9 Application to Pharmacokinetic Analysis.- 9.1 Introduction.- 9.2 Pharmacokinetic Model.- 9.3 Monte Carlo Estimation of Maximum Log Likelihood.- 9.4 Example.- 9.5 Concluding Remarks.- 10 State Space Modeling of Switching Time Series.- 10.1 Introduction.- 10.2 Time Series Data with Pulses and the Existing Methods.- 10.3 The State Space Model for Time Series with Pulses.- 10.4 Conclusions.- 11 Time Varying Coefficient AR and VAR Models.- 11.1 Introduction.- 11.2 Time Varying Coefficient AR Models.- 11.3 Time Varying Coefficient VAR Models.- 11.4 An Example of Seismic Data Analysis.- 12 Statistical Control of Cement Process.- 12.1 Introduction.- 12.2 Cement Plant.- 12.3 Identification and Control of the Kiln Process.- 12.4 Collection and Identification of the Data under the On-line Control.- 12.5 Optimal Production Level and Pursuit Control.- 12.6 Conclusions.- 13 Analysis of a Human/2-Wheeled-Vehicle System by ARdock.- 13.1 Introduction.- 13.2 Data.- 13.3 AR Model and ARdock.- 13.4 Numerical Results.- 13.5 Analysis of the Hands-Free Steering.- 13.6 The Optimum Control.- 14 Vibration Data Analysis of Automobiles.- 14.1 Preface.- 14.2 Road Surface Input-Wear of Component Material and Riding Comfort.- 14.3 Separation of Correlated Power Components in a Multiple Input System by Means of Power Contributions.- 14.4 Decision of Continuity of Data Properties.- 14.5 Identification of Nonlinear Vibration System Through Bispectral Analysis.- 14.6 Continuous Measurement of Time-variant Spectrum.- 14.7 Afterword.- 15 Auto-Regressive Spectral Analysis of RR-Interval Time Series in Healthy Fetus and Newborn Infants.- 15.1 Introduction.- 15.2 Subjects and Methods.- 15.3 Results.- 15.4 Discussion.- 15.5 Conclusions.- 16 Information Processing Mechanisms in the Mammalian Brain: Analysis of Spatio-Temporal Neural Response in the Auditory Cortex.- 16.1 Introduction.- 16.2 Instrumentation of and Information Processing in the Brain.- 16.3 Optical Multipoint Observation in the Mammalian Auditory Cortex.- 16.4 Spatio-Temporal Neural Activity Observation.- 16.5 Functional Modules in the Auditory Cortex.- 16.6 Pattern Time Series Analysis.- 16.7 Neural Correlation of and Neural Binding.- 16.8 Evaluation of Cortical Neural Binding.- 16.9 Characteristics of Stationary Stochastic Response.- 16.10 Conclusions.- 17 Time Series Analysis of Financial Asset Price Fluctuations.- 17.1 Introduction.- 17.2 Nonstationary Nature of Financial Asset Prices.- 17.3 Multivariate Analysis of the Time Series Model.- 17.4 Conclusion.- 18 Dynamic Analysis of Economic Time Series.- 18.1 Introduction.- 18.2 Trend of the Economic Time Series and the Fluctuation Around the Trend.- 18.3 Analysis of Abrupt Change of Trend.- 18.4 Analysis of the Economic System by a Multivariate Nonstationary Time Series Model.- 18.5 Conclusions.- 19 Processing of Time Series Data Obtained by Satellites.- 19.1 Introduction.- 19.2 Problems to be Dealt With.- 19.3 Approach by a Bayesian Model -Simple Model.- 19.4 Example of a Simple Model.- 19.5 Point Noise Source Model.- 19.6 Conclusions.- 20 Analysis of Earth Tides Data.- 20.1 What are Earth Tides?.- 20.2 Analysis Model.- 20.3 Tidal Analysis Program BAYTAP-G.- 20.4 Focal Points in the Analysis.- 20.5 Concluding Remarks.- 21 Detection of Groundwater Level Changes Related to Earthquakes.- 21.1 Introduction.- 21.2 Observation Data.- 21.3 Data Analysis Method.- 21.4 Analysis of Actual Data.- 21.5 Conclusions.- 22 Processing of Missing Observations and Outliers in Time Series.- 22.1 Missing Observations and Outliers.- 22.2 Processing of Missing Observations.- 22.3 Processing of Outliers.- 22.4 Conclusions.- 23 Mental Preparation for Time Series Analysis.- 23.1 Introduction.- 23.2 Time Series Analysis and Statistical Science.- 23.3 Prediction and Expectation.- 23.4 Ultimate Truth and Models.- 23.5 Evaluation of a Model and Information Criterion.- 23.6 Confirmation of Validity.- 23.7 Conclusions.
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