# Course

## M485 - Time Series Analysis

Course No:
M485
Credit:
4
Prerequisites:
M206, M305
Approval:
UG-Elective
Syllabus:

Examples and objectives of time series, stationary time series and autocorrelation function, estimation and elimination of trend and seasonal components, testing for noise sequence, moving average process, autoregressive processes and ARMA processes, estimation of autocorrelation function, methods of forecasting-Durbin-Levinson algorithm and Innovations algorithm, the Wold decomposition, ARMA models-the auto-covariance and partial auto-covariance function, forecasting ARMA processes, spectral analysis-spectral densities, periodogram, modeling with ARMA processes, Yule-Walker estimation, maximum likelihood estimation, diagnostic checking, non-stationary time series-ARIMA models, identification techniques, forecasting ARIMA models, seasonal ARIMA models, multivariate time series, ARCH and GARCH models.

Reference Books:
1. Peter J. Brockwell and Richard A. Davis, “Introduction toTime Series and Forecasting”, Springer Texts in Statistics, 2010.
2. Chris. Chatfield, “The analysis of time series: An introduction”, 6th edition, Chapman & Hall/CRC, 2004.
3. J. D. Cryer and K.-S. Chan, “Time series analysis with applications in R”, 2nd edition, Springer, 2008.
4. R. H. Shumway and D. S. Stoffer, “Time series analysis and its applications with R examples”, 3rd edition, Springer, 2011.