Applied time series analysis with R

✍ Scribed by Elliott, Alan C.; Gray, Harry L.; Woodward, Wayne A

Year: 2017
Tongue: English
Leaves: 635
Edition: Second edition
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

Stationary time series -- 2. Linear filters -- 3. ARMA time series models -- 4. Other stationary time series models -- 5. Nonstationary time series models -- 6. Forecasting -- 7. Parameter estimation -- 8. Model identification -- 9. Model building -- 10. Vector-valued (multivariate) time series -- 11. Long-memory processes -- 12. Wavelets -- 13. G-Stationary processes

Abstract: 1. Stationary time series -- 2. Linear filters -- 3. ARMA time series models -- 4. Other stationary time series models -- 5. Nonstationary time series models -- 6. Forecasting -- 7. Parameter estimation -- 8. Model identification -- 9. Model building -- 10. Vector-valued (multivariate) time series -- 11. Long-memory processes -- 12. Wavelets -- 13. G-Stationary processes

✦ Table of Contents

Content: Stationary Time Series. Linear Filters. ARMA Time Series Models. Other Stationary Time Series Models. Nonstationary Time Series Models. Forecasting. Parameter Estimation. Model Identification. Model Building. Vector-Valued (Multivariate) Time Series. Long-Memory Processes. Wavelets. G-Stationary Processes.

✦ Subjects

Time-series analysis.;R (Computer program language);Time-series analysis;Zeitreihenanalyse

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