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Time Series Analysis

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.

Prerequisite: 
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

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