| Abstract: |
The course introduces the concepts and methods of time'series analysis. Specifically, the topics include (i) stationarity and ergodicity (ii) auto', cross' and partial'correlation functions (iii) linear random processes ' definitions (iv) auto'regressive, moving average, ARIMA and seasonal ARIMA models (v) spectral (Fourier) analysis and periodicity detection and (vi) parameter estimation concepts and methods. Practical implementations of the methods in R are illustrated throughout.nsport rates in chemical processes, and gives rise to many of the empirical correlations used in chemical engineering design. |
Electronics & Communication Engineering
Applied Time-Series Analysis
