Time Series Modelling and Forecasting

Swayam Prabha Course DTH Channel 16

The forty hours course is for the students in Bachelor's and Master program and covers the topics of Time Series Analysis.

Suggested books:

Box, George E. P., Gwilym M. Jenkins, Gregory C. Reinsel, Greta M. Ljung. (2015). Time Series Analysis, Forecasting and Control, Wiley.

Brockwell, P.J. and R.A. Davis, (2009). Time Series: Theory and Methods (Second Edition), Springer-Verlag.

Chatfield, C. (1975). The Analysis of Time Series: Theory and Practice Springer-Verlag.

Cowpertwait, Paul S.P and Andrew V. Metcalfe (2008). Introductory Time Series with R, Springer

Cryer, Jonathan D. Kung-Sik Chan (2008). Time Series Analysis: With Applications in R, Springer Texts in Statistics.

Granger, C.W.J. and M. Hatanka, (1964). Spectral Analysis of Economic Time Series, Princeton Univ. Press, N.J.

Kirchgassner, G. and J. Wolters, (2007). Introduction to Modern Time Series Analysis, Springer.

Montgomery, D.C. and L.A. Johnson (1977). Forecasting and Time Series Analysis, McGraw Hill.

Language of the course: English

Duration of the course: 40 Hours

YouTube link: The telecasted lectures are available at YouTube https://www.youtube.com/playlist?list=PLqMl6r3x6BUSP2fYr2rd3NMRTT4_5uTvV

Slides and Videos used in the lectures (download links): 

Lecture videos   Lecture slides       Lecture Title

Lecture 1          Lecture 1          Introduction to Time Series

Lecture 2           Lecture 2          Matrix Algebra and Regression Model

Lecture 3           Lecture 3          Components of time series

Lecture 4     Lecture 4          Components of time series

Lecture 5           Lecture 5          Components of time series

Lecture 6           Lecture 6          Components of time series

Lecture 7           Lecture 7          Smoothing and Adaptive Forecasting

Lecture 8           Lecture 8          Time series processes

Lecture 9           Lecture 9          Time series processes

Lecture 10         Lecture 10        Time series processes

Lecture 11         Lecture 11        Stationary Processes for Time Series Modelling

Lecture 12         Lecture 12        Stationary Processes for Time Series Modelling

Lecture 13         Lecture 13        Stationary Processes for Time Series Modelling

Lecture 14         Lecture 14        Stationary Processes for Time Series Modelling

Lecture 15         Lecture 15        Stationary Processes for Time Series Modelling

Lecture 16         Lecture 16        Estimation of Parameters

Lecture 17         Lecture 17        Estimation of Parameters

Lecture 18         Lecture 18        Frequency Domain Analysis

Lecture 19         Lecture 19        Frequency Domain Analysis

Lecture 20         Lecture 20        Frequency Domain Analysis

Lecture 21         Lecture 21        Frequency Domain Analysis

Lecture 22         Lecture 22        Frequency Domain Analysis

Lecture 23         Lecture 23        Forecasting with Stationary Processes

Lecture 24         Lecture 24        Forecasting with Stationary Processes

Lecture 25         Lecture 25        Diagnostics Checking

Lecture 26         Lecture 26        Non-Stationary and Long Memory Processes

Lecture 27         Lecture 27        Nonstationary and Long Memory Processes

Lecture 28         Lecture 28        Nonstationary and Long Memory Processes

Lecture 29         Lecture 29        Non-Stationary and Long Memory Processes

Lecture 30         Lecture 30        Non-Stationary and Long Memory Processes

Lecture 31         Lecture 31        Multivariate Time Series Processes

Lecture 32         Lecture 32        Multivariate Time Series Processes

Lecture 33         Lecture 33        Multivariate Time Series Processes

Lecture 34         Lecture 34        Multivariate Time Series Processes

Lecture 35         Lecture 35       Multivariate Time Series Processes

Lecture 36         Lecture 36    Multivariate Time Series Processes

Lecture 37         Lecture 37        Multivariate Time Series Processes

Lecture 38         Lecture 38        Multivariate Time Series Processes

Lecture 39         Lecture 39        Stochastic Volatility Models: ARCH, GARCH Processes

Lecture 40         Lecture 40      Stochastic Volatility Models: ARCH, GARCH Processes