1 historical series data
1.1 Purpose
1.2 Time series
1.3 R language
1.4 Graphs, trends and seasonal variations
1.4.1 Departure on the flight: reservations for air passengers
1.4.2 Unemployment: Maine
1.4.3 Multiple time series: data on electricity, beer and chocolate
1.4.4 Quarterly exchange rate: from GBP to NZ $
1.4.5 Global temperature series
1.5 Standard decomposition
1.5.1 Notation
1.5.2 Models
1.5.3 Estimation of seasonal trends and effects
1.5.5 Decomposition in R
1.6 Summary of the commands used in the examples

2 correlation
2.1 Purpose
2.2 Expectation and together
2.2.1 Expected value
2.2.2 The set and the stationarity
2.2.4 Variance function
2.2.5 Autocorrelation
2.3 The correlogram
2.3.1 General discussion
2.3.2 Example based on series of air passengers
2.4 Covariance of sums of random variables
2.5 Summary of the commands used in the examples

3 forecasting strategies
3.1 Purpose
3.4.1 Exponential leveling

4 basic stochastic models
4.1 Purpose
4.2 White noise
4.2.1 Introduction
4.2.2 Definition
4.2.3 Simulation in R
4.2.4 Second order properties and the correlogram
4.2.5 Adaptation of a white noise model
4.3 Casual walks
4.3.1 Introduction
4.3.2 Definition
4.3.3 The backward shift operator
4.3.4 Random walk: second order property
4.3.5 Derivation of second order properties *
4.3.7 Simulation
4.5 Autoregressive models
4.5.1 Definition
4.5.2 Stationary and non-stationary AR processes
4.5.3 Second order properties of an AR model (1)
4.5.5 Correlogram of an AR process (1)
4.5.6 partial autocorrelation
4.5.7 Simulation

4.6 Mounted models
4.6.1 Model mounted on simulated series
4.6.2 Exchange rate series: AR model fitted
4.6.3 Global temperature series: mounted AR model
4.7 Summary of commands R
5 regression
5.1 Purpose
5.2 Linear models
5.2.1 Definition
5.2.2 Stationarity
5.2.3 Simulation
5.3 Mounted models
5.3.1 Model adapted to simulated data
5.3.2 Model adapted to the temperature series (1970-2005)
5.3.3 Autocorrelation and estimation of sample statistics *
5.4 Generalized minimum squares
5.4.1 GLS suitable for the simulated series
5.4.2 Confidence interval for the temperature trend
5.5 Linear models with seasonal variables
5.5.1 Introduction
5.5.2 Variables of the additive seasonal indicator
5.5.3 Example: seasonal model for temperature series
5.6 Seasonal harmonic models
5.6.1 Simulation
5.6.2 Suitable for simulated series
5.6.3 Harmonic model adapted to the temperature series (1970-2005)
5.7 Logarithmic transformations
5.7.1 Introduction
5.7.2 Example using the series of air passengers
5.8 Non-linear models
5.8.1 Introduction
5.8.2 Example of a simulated and adapted nonlinear series
5.9 Forecast from regression
5.9.1 Introduction
5.9.2 Prediction in R
5.10 Reverse transformation and diagonal correction
5.10.1 Normal residual errors in the register
5.10.2 Empirical correction factor for the means of forecasting
5.10.3 Example using air passenger data
5.11 Summary of commands R

6 stationary models
6.1 Purpose
6.2 Strictly fixed series
6.3 Average mobile models
6.3.1 Process MA (q): definition and properties
6.3.2 Examples R: Correlogram and simulation
6.4 MA models fitted
6.4.1 Model mounted on simulated series
6.4.2 Exchange rate series: MA model fitted
6.5 Mixed models: the ARMA process
6.5.1 Definition
6.6 ARMA models: empirical analysis
6.6.1 Simulation and adaptation
6.6.2 Exchange rate series
6.6.4 Data from the wave tank
6.7 Summary of commands R