Time Series Analysis
611
611
An introduction to time series analysis with an emphasis on mathematical understanding and its software implementation. Programming uses Python.
Text:
Introduction to Time Series and Forecasting, Brockwell and Davis, Springer, 3rd ed.
Credit Hours:
3
TOPICS:
- Modeling time series, trend, seasonality and residual process
- Autocovariance function, multivariate time series, moving average and autoregression
- Stationary processes, linear processes, linear filtering
- Confidence intervals for the mean and the autocorrelation, hypothesis tests for a time series model
- ARMA models, partial autocorrelation function, parameter estimation methods, forecasting, model selection
- Stationary processes in the frequency domain, spectral density, periodogram, smoothing, spectral window
- Nonstationary time series, ARIMA models
- State-space representation, Kalman recursions
- Recurrent neural networks as time allows
(Talata 2021 )
Frequency:
Odd Spring Semesters Only
