The data | Automatic model | Specifying the structure | Distribution | Initialisation | Provided parameters | Explanatory variables and intermittent demand | Methods | References

Methods for the class smooth

Supported distributions | Density functions of continuous distributions | Normal distribution | Laplace distribution | Asymmetric Laplace distribution | S distribution | Generalised Normal distribution | Logistic distribution | Student t distribution | An example of application | Density functions for continuous non-negative data | Box-Cox Normal distribution | Folded Normal distribution | Rectified Normal distribution | Density functions for continuous positive data | Log-Normal distribution | Inverse Gaussian distribution | Gamma distribution | Exponential distribution | Log-Laplace distribution | Log-S distribution | Log-Generalised Normal distribution | Continuous distributions on a specific interval | Logit-normal distribution | Beta distribution | Probability mass functions of discrete distributions | Poisson distribution | Negative Binomial distribution | Binomial distribution | Geometric distribution | Cumulative functions for binary variables | Cumulative Logistic distribution | Cumulative Normal distribution | Mixture distribution models | Scale model | Using different loss functions | Estimation process | References

Introduction | Quick start: AirPassengers | The PTS model string | Power (P) | Trend (T) | Seasonal (S) | Automatic selection | ARMA on the irregular component | Forecasting | Outlier detection | External regressors | A pure structural-break example | Holdout evaluation and accuracy | Simulation | Inspecting the fit | Further reading

Main functions | Model evaluation functions | Marketing analytics tools | Methods | Exogenous variables transformation tools | Distribution functions | Additional functions | xregExpander | stepwise | calm | References

The data | om(): ETS occurrence model | om(): ARIMA occurrence model | om(): Regression occurrence model | omg(): the general two-component occurrence model | auto.om(): automatic occurrence type selection | See also | References

The basics | iETS$_F$ | iETS$_O$ | iETS$_I$ | iETS$_D$ | iETS$_G$ | iETS$_A$ | References

Exponential Smoothing | SARIMA | Complex Exponential Smoothing | Generalised Univariate Model | Simple Moving Average

ADAM ETS | ADAM ARIMA | ADAM ETSX / ARIMAX / ETSX+ARIMA | Auto ADAM

References

How can this be done? | What does this have to do with R? | Making things more ~~exciting~~ complicated | Making things even more ~~complicated~~ exciting | References

Simulate data from VES | References

MSARIMA | References

Methods for the class legion

Analysing the relation between the two variables in categorical scales | Cramer's V | Plotting categorical variables | Association between the categorical and numerical variables | Association between several variables | Measures of association | Partial correlations | Plots of association between several variables | Regression diagnostics