In order to learn Julia I began building the package Forecast, after familiarizing myself with the language it was just natural to move from adding new models to the package to refactor it so that the package could benefit from some of the great features that Julia has to offer.

In particular, I am going to divide Forecast into several smaller packages since expanding functionality in Julia packages comes natural to the language. The first of these packages is Smoothers.

The package Smoothers provides a collection of smoothing heuristics, models and smoothing related applications. The current available smoothers and applications are:

- Henderson Moving Average Filter (
**hma**) - Linear Time-invariant Difference Equation Filter (
**filter**) - Matlab/Octave - Locally Estimated Scatterplot Smoothing (
**loess**) - Seasonal and Trend decomposition based on Loess (
**stl**) - Simple Moving Average (
**sma**)

**Note**: `Forecast: stl, loess`

return exact solutions, the implementation for `Smoothers: stl, loess`

are fast (much faster) implementations but still offer the possibility for exact solutions.