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+---
+output: github_document
+---
+
+<!-- README.md is generated from README.Rmd. Please edit that file -->
+
+```{r, include = FALSE}
+knitr::opts_chunk$set(
+  collapse = TRUE,
+  comment = "#>",
+  fig.path = "man/figures/README-",
+  out.width = "100%"
+)
+```
+
+# FastForecast
+
+<!-- badges: start -->
+<!-- badges: end -->
+
+The goal of FastForecast is to provide a fast and accurate forecasting method for time series data.This package takes a dataframe of several time series as input, and can correct for atypical points, seasonality and stationarity. It then generates forecasting models using 6 econometric models and 6 Machine-Learning models. It also allows you to display series graphically, calculate forecast quality indicators and display them in table form as well as graphically.
+
+## Installation
+
+You can install the development version of FastForecast from [GitHub](https://github.com/) with:
+
+``` r
+# install.packages("devtools")
+devtools::install_github("NoaLRX/FastForecast")
+```
+
+## Example
+
+This is a basic example which shows you how to solve a common problem:
+
+```{r example}
+library(FastForecast)
+## basic example code
+```
+
+What is special about using `README.Rmd` instead of just `README.md`? You can include R chunks like so:
+
+```{r cars}
+summary(cars)
+```
+
+You'll still need to render `README.Rmd` regularly, to keep `README.md` up-to-date. `devtools::build_readme()` is handy for this.
+
+You can also embed plots, for example:
+
+```{r pressure, echo = FALSE}
+plot(pressure)
+```
+
+In that case, don't forget to commit and push the resulting figure files, so they display on GitHub and CRAN.