1. Time Series Analysis.
1.1 Introduction. Characteristics of a time series: Trend, homoscedasticity and seasonal cycle.
1.2 Stationary Time Series.
1.3 Transformation on non Stationary Time Series into Stationary Time Series.
1.4 Simple and partial autocorrelation function.
1.5 Models AR (1) AR (2) and AR (p)
1.6 Models MA (1), MA (2) and MA (q)
1.7 ARMA Models
1.8 ARIMA Models
1.9 Estimation and diagnosis.
1.11 Seasonal ARIMA Models
12.1 Forecasting with seasonal ARIMA models
2.1 Introduction to duration data (ADS)
2.1 Functions used: reliability function and failure rate
2.3 Types of failure rates.
2.4 Parametric models: Weibull
2.5 Graphical Methods to determinate the model.
2.6 Duration estimation for complete data.
2.7 Censored Data. Types of censorship.
2.8 Graphical methods for censored data. Kaplan Meier Estimator
2.9 Parametric Estimation with censored data.
2.10 Accelerated tests (under stress)
11.2 Series and parallel systems. Introduction to complex systems.