[This article was first published on Archives R - Open Forecasting, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.Good news! The smooth package v4.3.0 is now on CRAN. And there are several things worth mentioning, so I have written this post.New default initialisation mechanismSince the beginning of the package, the smooth functions supported three ways for initialising the state vector (the vector that includes level, trend, seasonal indices): optimisation, backcasting and values provided by user. The former has been considered the standard way of estimating ETS, while the backcasting was originally proposed by Box & Jenkins (1970) and was only implemented in the smooth (at least, I haven’t seen it anywhere else). The main advantage of the latter is in computational time, because you do not need to estimate every single value of the state vector. The new ADAM core that I developed during COVID lockdown, had some improvements for the backcasting, and I noticed that adam() produced more accurate forecasts with it than with the optimisation. But I needed more testing, so I have not changed anything back then.However, my recent work with Kandrika Pritularga on capturing uncertainty in ETS, have demonstrated that backcasting solves some fundamental problems with the variance of states – the optimisation cannot handle so many parameters, and asymptotic properties of ETS do not make sense in that case (we’ll release the paper as soon as we finish the experiments). So, with this evidence on hands and additional tests, I have made a decision to switch from the optimisation to backcasting as the default initialisation mechanism for all the smooth functions.The final users should not feel much difference, but it should work faster now and (hopefully) more accurately. If this is not the case, please get in touch or file an issue on github.Also, rest assured the initial="optimal" is available and will stay available as an option in all the smooth functions, so, you can always switch back to it if you don’t like backcasting.Finally, I have introduce a new initialisation mechanism called “two-stage”, the idea of which is to apply backcasting first and then to optimise the obtained state values. It is slower, but is supposed to be better than the standard optimisation.ADAM coreEvery single function in the smooth package now uses ADAM C++ core, and the old core will be discontinued starting from v4.5.0 of the package. This applies to the functions: es(), ssarima(), msarima(), ces(), gum(), sma(). There are now the legacy versions of these functions in the package with the prefix “_old” (e.g. es_old()), which will be removed in the smooth v4.5.0. The new engine also helped ssarima(), which now became slightly more accurate than before. Unfortunately, there are still some issues with the initialisation of the seasonal ssarima(), which I have failed to solve completely. But I hope that over time this will be resolved as well.smooth performance updateI have applied all the smooth functions together with the ets() and auto.arima() from the forecast package to the M1, M3 and Tourism competition data and have measured their performances in terms of RMSSE, scaled Cumulative Error (sCE) and computational time. I used the following R code for that:Long and boring code in Rlibrary(Mcomp)library(Tcomp)library(forecast)library(smooth)# I work on Linux and use doMC. Substitute this with doParallel if you use Windowslibrary(doMC)registerDoMC(detectCores())# Create a small but neat function that will return a vector of error measureserrorMeasuresFunction