Time Series 1

Objectives

Know the main linear models used and their characteristics.
Estimate model parameters and test their validity.
Recognize the main characteristics of a time series using the usual graphical tools.
Conduct a complete statistical process: find suitable models, check their validity for the data and forecast future values.

Course outline

Trend, seasonality and linear filtering.
Stationary processes, autocovariance, autocorrelation.
ARMA processes, causality, invertibility, innovation, estimation.
Box-Jenkins method, non-stationary (S)ARIMA processes, unit root test.
Forecasting: best linear predictor, exponential smoothing.
Exogenous contributions, ARMAX processes and cross-correlation. Heteroskedasticity and (G)ARCH processes.

Prerequisites

Probability and inferential statistics