Univariate time series forecasting (autoregressive models) and recursive forecasts | Recursive forecasts | The utsf package | Prediction intervals | Supported models | Using your own models | Example 1: k-nearest neighbors | Example 2: random forest and neural networks | Setting the parameters of the regression models | Setting the paramaters of a regression model supplied by the user | Example 2: random forest | Estimating forecast accuracy | Parameter tuning | Preprocessings and transformations | Differencing | The additive transformation | The multiplicative transformation | Default parameters | More on using your custom models

Introduction | Time series forecasting with KNN regression | Multi-step ahead strategies | The Multiple Input Multiple Output strategy | The recursive strategy | Additional features | Combination and distance function | Combining several models with different k parameters | Forecasting time series with a trend | Automatic forecasting | Evaluating the model | Conclusions | References

Visualizing a single time series | Visualizing several forecasts | Creating collections of time series | Visualizing a time series from a collection | The web-based GUI for visualizing collections of time series

Introduction | Time series forecasting with GRNN | Multiple step ahead strategies | Multiple Input Multiple Output strategy | The recursive strategy | Assessing the model | Preprocessing and model selection | Model selection | The smoothing parameter | The autoregressive lags | Multi-step ahead strategy | Transformations | Detrending | Seasonality | Acknowledgements