## smooth

What R package do you use when you want to produce forecasts for a time series? The answer would probably be [crayon-5e8acd3851bf5570755309-i/]. While this is a very good package, that contains a lot of useful functions, some of them are rather restrictive. [crayon-5e8acd3851c02825658230-i/] package for R contains several more flexible functions for forecasting purposes. The list of functions includes:
[crayon-5e8acd3851c05021273313-i/] - the function that implements ETS (exponential smoothing in Error-Trend-Seasonal taxonomy) model. The advantages of the function in comparison with [crayon-5e8acd3851c07909034934-i/] function from [crayon-5e8acd3851c09978682851-i/] package are discussed here;
[crayon-5e8acd3851c0a200653270-i/] - state-space ARIMA model, the function that implements versatile and flexible ARIMA that allows estimating simple and seasonal ARIMA using advanced methods;
[crayon-5e8acd3851c0c864865371-i/] - Multiple Seasonal ARIMA, that allows constructing models with several seasonalities (e.g. for high frequency data) in a reasonable time;
[crayon-5e8acd3851c0e854913030-i/] - Complex Exponential Smoothing, model that side steps the ETS taxonomy and allows producing non-linear forecasting trajectories;
[crayon-5e8acd3851c10122637976-i/] - Generalised Univariate Model - model that underlies them all;
[crayon-5e8acd3851c12870002015-i/] and [crayon-5e8acd3851c14687088490-i/] -...