AI364

Course Name: 

Time Series Analysis (AI364)

Programme: 

B.Tech (AI)

Category: 

Programme Specific Electives (PSE)

Credits (L-T-P): 

(3-0-2) 4

Content: 

Stationary processes, ensemble, random walk Vs trend, periodicity, linear process; Estimators mean, ACF, PACF, variogram ;Properties covariance , normality ; Regression , models for trend, differencing, backshift operator ; Harmonic regression, periodogram, signal processing; Nonparametric regression, smoothing, periodic functions; Model selection, AIC, BIC, SIC, bias-variance trade-off; ARMA models; Estimation , MLE, LS, forward-backward ; State-space models ,Kalman filter, hidden state, HMM, Switching models, hidden Markov models (HMM), GARCH, stochastic volatility, financial models; Heteroscedasticity, Wavelets Vector Autoregressive (VAR) Models, Integrated Variables and Cointegrated VAR Models, Time-varying parameter and Bayesian VARs, Multivariate GARCH Models

References: 

Shumway, R.H. and Stoffer, D.S., Time Series Analysis and its Applications: With R Examples, Springer.
Pole A., West M. and Harrison P.J., Applied Bayesian Forecasting and Time Series Analysis. Chapman-Hall.
Tsay, R. S. Analysis of Financial Time Series, John Wiley and Sons .
West, M. and Harrison, P.J. (1997), Bayesian Forecasting and Dynamic Models,Springer-Verlag.

Department: 

Information Technology
 

Contact us

Head of the Department,
Department of Information Technology,
National Institute of Technology Karnataka,
SurathkalP. O. Srinivasnagar, Mangalore - 575 025
Ph.:    +91-824-2474056
Email:  hodit [at] nitk [dot] edu [dot] in
 

Web Admin: Sowmya Kamath S

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