This book is a comprehensive introduction to all the major time-series techniques, both time-domain and frequency-domain.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Preface Part I. Overview: 1. The search for hidden structures 2. The ubiquitous cycles 3. How Slutzky created order from chaos 4 Forecasting: Yule's autoregressive models 5. Into the black box with white light 6. Experimentation and change Part II. Time-series models: 7. Models and the problem of correlated data 8. An introduction to time-series models: stationarity 9. What if the data are not stationary? Part III. Deterministic and nondeterministic components: 10. Moving-average models 11. Autoregressive models 12. The complex behaviour of the second-order autoregressive process 13. The partial autocorrelation function: completing the duality 14. The duality of MA and AR processes Part IV. Stationary frequency-domain models: 15. The spectral density function 16. The periodogram 17. Spectral windows and window carpentry 18. Explanation of the Slutzky effect Part V. Estimation in the time domain: 19. AR model fitting and estimation 20. Box-Jenkins model fitting: the ARIMA models 21. Forecasting 22. Model fitting: worked example Part VI. Bivariate time-series analysis: 23. Bivariate frequency-domain analysis 24. Bivariate frequency example: mother-infant play 25. Bivariate time-domain analysis Part VII. Other Techniques: 26. The interrupted time-series experiment 27. Multivariate approaches Notes References Index.
Preface Part I. Overview: 1. The search for hidden structures 2. The ubiquitous cycles 3. How Slutzky created order from chaos 4 Forecasting: Yule's autoregressive models 5. Into the black box with white light 6. Experimentation and change Part II. Time-series models: 7. Models and the problem of correlated data 8. An introduction to time-series models: stationarity 9. What if the data are not stationary? Part III. Deterministic and nondeterministic components: 10. Moving-average models 11. Autoregressive models 12. The complex behaviour of the second-order autoregressive process 13. The partial autocorrelation function: completing the duality 14. The duality of MA and AR processes Part IV. Stationary frequency-domain models: 15. The spectral density function 16. The periodogram 17. Spectral windows and window carpentry 18. Explanation of the Slutzky effect Part V. Estimation in the time domain: 19. AR model fitting and estimation 20. Box-Jenkins model fitting: the ARIMA models 21. Forecasting 22. Model fitting: worked example Part VI. Bivariate time-series analysis: 23. Bivariate frequency-domain analysis 24. Bivariate frequency example: mother-infant play 25. Bivariate time-domain analysis Part VII. Other Techniques: 26. The interrupted time-series experiment 27. Multivariate approaches Notes References Index.
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