JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD

Course Objectives:

To impart knowledge about the areas of time series statistics. To apply the concepts of time series statistics to real data sets. To enable the students to assimilate data applied to real, scientific and interesting problems.

Course Outcomes

Upon successful completion of the course, the students will be able to:

Understand and analyze the theoretical & practical aspects of time series data. Understand the components of time series and measure these components. Identify an appropriate time series model to fit the empirical data and use it for forecasting. Understand the genesis of the multivariate time series analysis.

UNIT - I

Introduction: Time-series as discrete parameter stochastic process, auto covariance and autocorrelation functions and their properties.

UNIT - II

Exploratory Time Series Analysis: Components of time series, Messurements of trend, Measurement of seasonal fluctuation, Mesurement of cyclic movement, tests for trend and seasonality, exponential and moving average smoothing. Holt and Winters smoothing, forecasting based on smoothing.

UNIT - III

Models for Stationary Time Series: (1) moving average (MA), (2) auto regressive (AR), (3) ARMA and (4) AR integrated MA (ARIMA) models. Box-Jenkins models, choice of AR and MA periods.

UNIT - IV

Modeling Seasonal Time Series: Seasonal ARIMA models, estimation and forecasting, Fitting ARIMA models with Box-Jenkins procedure, Identification, Estimation, Verification, Test for white noise, Forecasting with ARMA models. Spectral analysis: Spectral analysis of weakly stationary process, peridogram and correlogram analyses, computations based on Fourier transform.

UNIT - V

Multivariate Time Series: Stationary multivariate time series, Cross-covariance and Cross-Correlation Matrices, Covariance Stationary, Vector white nose process, Moving average representation of a stationary vector process

Text Books:

Time Series Analysis: Forecasting and Control by G. Box and Jenkins, Palgrave Macmillan. Introduction to Time Series and Forecasting by P.J. Brockwell and R.A. Davis, Springer. Time Series Analysis by J. D. Hamilton, Princeton University Press.

Reference Book:

Introduction to Time Series by Becketti, Sean (2013) Using Stata. Stata Press, College Station, Texas, ISBN/ISSN: 978-1-59718-132-7. M.Tech. (Data Science) II Year I Semester