## smooth

What R package do you use when you want to produce forecasts for a time series? The answer would probably be [crayon-5ed6466b04656079482297-i/]. While this is a very good package, that contains a lot of useful functions, some of them are rather restrictive. [crayon-5ed6466b04661089053374-i/] package for R contains several more flexible functions for forecasting purposes. The list of functions includes:
[crayon-5ed6466b04664389668473-i/] - the function that implements ETS (exponential smoothing in Error-Trend-Seasonal taxonomy) model. The advantages of the function in comparison with [crayon-5ed6466b04666493928224-i/] function from [crayon-5ed6466b04668135932111-i/] package are discussed here;
[crayon-5ed6466b0466a458329383-i/] - state-space ARIMA model, the function that implements versatile and flexible ARIMA that allows estimating simple and seasonal ARIMA using advanced methods;
[crayon-5ed6466b0466c595257525-i/] - Multiple Seasonal ARIMA, that allows constructing models with several seasonalities (e.g. for high frequency data) in a reasonable time;
[crayon-5ed6466b0466d069996343-i/] - Complex Exponential Smoothing, model that side steps the ETS taxonomy and allows producing non-linear forecasting trajectories;
[crayon-5ed6466b0466f419573522-i/] - Generalised Univariate Model - model that underlies them all;
[crayon-5ed6466b04671314169858-i/] and [crayon-5ed6466b04674492870242-i/] -...