EC862 Time Series Analysis and Data Science

Course Name: 

EC862 Time Series Analysis and Data Science




Elective (Ele)

Credits (L-T-P): 

(4-0-0) 4


Identifying patterns in time series data, inference, estimation, prediction, general properties of time series models, systematic pattern and random noise, trend and seasonality analysis, time domain and frequency domain analysis, data visualization, linear and mixed models, AR models, ARMA models, ARIMA models, identification and parameter estimation, model estimation and forecasting, Akaike information criterion, mixed models, single spectrum and cross spectrum analysis, higher order statistics, state space models, Kalman filter, non-Gaussian linear models, Generalized autoregressive conditional hetereoskedastic (GARCH) models, stochastic volatility models, extreme value theory, nonlinear time series models, applications in data science.


Peter J. Brockwell, Richard A. Davis, Introduction to Time Series and Forecasting, Springer, 2001. James Fahl, Data Analytics, Paperback, 2017.
George E. P. Box, Gwilym M. Jenkins, Gregory C. Reinsel, Time Series Analysis: Forecasting and Control 4th Ed, Wiley, 2008.
Andrew C. Harvey, Forecasting, Structural Time Series Models and the Kalman Filter, Reprint Ed, 2001.
Box-Steffensmeier, Janet M., John R. Freeman, Matthew P. Hitt, Jon C. W. Pevehouse, Time Series Analysis for the Social Sciences, Cambridge University Press. 2014.
James Fahl, Data Analytics, Paperback, 2017.


Electronics and Communication Engineering(ECE)

Contact us

Dr. T. Laxminidhi,  Professor and Head, 
Department of E&C, NITK, Surathkal
P. O. Srinivasnagar,
Mangalore - 575 025 Karnataka, India.

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