Time Series Analysis and Forecasting

Stochastic and deterministic mathematical models for forecasting of time series, Classical techniques in Time Series Analysis, Different Smoothing Techniques, General linear process, Autoregressive process AR(P), Moving average process Ma(q): Autocorrelation, Partial autocorrelation, and Spectrum, Linear stationary models for autoregressive moving average processes, Mixed autoregressive moving average processes, Autocovariances, Autoregressive Integrated Moving Average Processes (ARIMA). Forecasting, Calculating and updating forecast, Forecast function and forecast weight, Examples of forecast functions and their updating, Identification in time domain, Model Identification , Estimation of Parameters, Model estimation, Non-linear estimation, Model diagnostic checks, Seasonal models, Transfer function models, Discrete transfer function model, continuous dynamic models, Identification, fitting and checking of transfer function models, Intervention analysis models and outlier detection, Estimation of Autoregressive moving average (ARMA) models, Use of time series techniques in Engineering fields.