Course

Unit I

Probability Models: White noise model, random walk, linear processes, Moving Average (MA), Autoregressive (AR), ARMA and ARIMA, seasonal ARIMA models. Invertibility. ACF and PACF of these processes. Sample ACF and PACF. Model identification. 

Unit II

Model Building: Estimation of mean, autocovariance function and autocorrelation function. Estimation of AR models – Yule-Walker equations, estimation of MA model and ARMA models. Order selection in AR and MA models.

Unit III

Forecasting: Forecast mean square error (FMSE), Least squares prediction. BLUP. Box-Jenkins forecasting. Forecasting through exponential smoothing and Holt-Winters smoothing. Residual analysis and diagnostic checking. Nonstationary time series models and their identification.

Course Outcomes:

CO1: Understand the concept of time series and its components

CO2: Understand the bases of different models of time series analysis including

decomposition

CO3: To learn proper model identification and its estimation.

CO4: To learn several ways of identifying the forecasting methods with the least forecasting error.

CO-PO Mapping:

Text Books/References Books:

1. Box, G.E.P. and Jenkins, G.M. (1976): Time series analysis—Forecasting and Control,

Holden-day, San Francisco.

2. Anderson, T.W. (1971): The Statistical Analysis of Time Series, Wiley, N.Y.
3. Montgemory, D.C. and Johnson, L.A. (1977): Forecasting and Time Series Analysis, .
4. Kendall, Sir Maurice and Ord, J.K. (1990): Time Series (Third Edition), Edward Arnold.
5. Brockwell, P.J. and Davis, R.A.: Time Series: Theory and Methods (Second Edition),

SpringerVerlag.

6. Fuller, W.A. (1976): Introduction to Statistical Time Series, John Wiley, N.Y.