Details of MA5109 (Autumn 2020)
| Level: 5 | Type: Theory | Credits: 4.0 |
| Course Code | Course Name | Instructor(s) |
|---|---|---|
| MA5109 | Time Series Analysis | Satyaki Mazumder |
| Syllabus |
|---|
| Introduction: Review of various components of time series, plots and descriptive statistics, discrete-parameter stochastic processes-- strong and weak stationarity, autocovariance and autocorrelation.
Spectral Analysis and Different Processes: Spectral analysis of weakly stationary processes-- periodogram, fast Fourier transform; Moving average, autoregressive, autoregressive moving average (ARMA) and autoregressive integrated moving average processes (ARIMA); Box-Jenkins model, state-space model. Forecasting and Model Selection: Linear filters, signal processing through filters, inference in ARMA and ARIMA models; Forecasting-- ARIMA and state-space models, Kalman filter; Model building-- residuals and diagnostic checking; Model selection-- strategies for missing data. Time-frequency Analysis: Short-term Fourier transform, wavelets, data analysis with computer packages. |
| Prerequisite |
|---|
| Statistical Inference (MA4107) and Functional Analysis (MA4102) |
| References |
|---|
| Suggested Texts:
1. Brockwell, P.J. and Davis, R.A., Introduction to Time Series and Forecasting, Springer. 2. Fuller, W.A., Introduction to Statistical Time Series, Wiley-Blackwell. 3. Shumway, R.H. and Stoffer, D.S., Time Series Analysis and Its Applications, Springer. |
Course Credit Options
| Sl. No. | Programme | Semester No | Course Choice |
|---|---|---|---|
| 1 | IP | 1 | Not Allowed |
| 2 | IP | 3 | Not Allowed |
| 3 | IP | 5 | Not Allowed |
| 4 | MR | 1 | Not Allowed |
| 5 | MR | 3 | Not Allowed |
| 6 | MS | 5 | Not Allowed |
| 7 | MS | 7 | Not Allowed |
| 8 | MS | 9 | Elective |
| 9 | RS | 1 | Elective |
| 10 | RS | 2 | Elective |