## smooth

What R package do you use when you want to produce forecasts for a time series? The answer would probably be [crayon-5e56fae9dddcb230661607-i/]. While this is a very good package, that contains a lot of useful functions, some of them are rather restrictive. [crayon-5e56fae9dddd7211893944-i/] package for R contains several more flexible functions for forecasting purposes. The list of functions includes:
[crayon-5e56fae9dddd9784533440-i/] - the function that implements ETS (exponential smoothing in Error-Trend-Seasonal taxonomy) model. The advantages of the function in comparison with [crayon-5e56fae9ddddb267525022-i/] function from [crayon-5e56fae9ddddd187274049-i/] package are discussed here;
[crayon-5e56fae9ddddf906731520-i/] - state-space ARIMA model, the function that implements versatile and flexible ARIMA that allows estimating simple and seasonal ARIMA using advanced methods;
[crayon-5e56fae9ddde1384846853-i/] - Multiple Seasonal ARIMA, that allows constructing models with several seasonalities (e.g. for high frequency data) in a reasonable time;
[crayon-5e56fae9ddde3342322651-i/] - Complex Exponential Smoothing, model that side steps the ETS taxonomy and allows producing non-linear forecasting trajectories;
[crayon-5e56fae9ddde5424133301-i/] - Generalised Univariate Model - model that underlies them all;
[crayon-5e56fae9ddde6131719767-i/] and [crayon-5e56fae9ddde8991764837-i/] -...