## smooth

What R package do you use when you want to produce forecasts for a time series? The answer would probably be [crayon-5e8ac7baa35bc054693481-i/]. While this is a very good package, that contains a lot of useful functions, some of them are rather restrictive. [crayon-5e8ac7baa35c8942125677-i/] package for R contains several more flexible functions for forecasting purposes. The list of functions includes:
[crayon-5e8ac7baa35cb227777581-i/] - the function that implements ETS (exponential smoothing in Error-Trend-Seasonal taxonomy) model. The advantages of the function in comparison with [crayon-5e8ac7baa35cd100453293-i/] function from [crayon-5e8ac7baa35cf840770960-i/] package are discussed here;
[crayon-5e8ac7baa35d1387009398-i/] - state-space ARIMA model, the function that implements versatile and flexible ARIMA that allows estimating simple and seasonal ARIMA using advanced methods;
[crayon-5e8ac7baa35d3698337442-i/] - Multiple Seasonal ARIMA, that allows constructing models with several seasonalities (e.g. for high frequency data) in a reasonable time;
[crayon-5e8ac7baa35d5267485075-i/] - Complex Exponential Smoothing, model that side steps the ETS taxonomy and allows producing non-linear forecasting trajectories;
[crayon-5e8ac7baa35d7292426068-i/] - Generalised Univariate Model - model that underlies them all;
[crayon-5e8ac7baa35d9565735936-i/] and [crayon-5e8ac7baa35db862204049-i/] -...