diff --git a/R/DESCRIPTION b/R/DESCRIPTION
index 44c6b73..b99208d 100644
--- a/R/DESCRIPTION
+++ b/R/DESCRIPTION
@@ -25,6 +25,8 @@ Imports:
     stats,
     tidyr (>= 0.6.1),
     utils,
+    purrr,
+    rlang,
     xts
 Suggests:
     knitr,
diff --git a/R/NAMESPACE b/R/NAMESPACE
index 1e460d9..86ba8ff 100644
--- a/R/NAMESPACE
+++ b/R/NAMESPACE
@@ -1,18 +1,25 @@
 # Generated by roxygen2: do not edit by hand
 
-S3method(plot,prophet)
 S3method(predict,prophet)
 export(add_regressor)
 export(add_seasonality)
+export(all_metrics)
 export(cross_validation)
 export(dyplot.prophet)
 export(fit.prophet)
 export(layer_changepoints)
+export(mae)
 export(make_future_dataframe)
+export(mape)
+export(me)
+export(mpe)
+export(mse)
+export(plot.prophet)
 export(plot_forecast_component)
 export(predictive_samples)
 export(prophet)
 export(prophet_plot_components)
+export(rmse)
 export(simulated_historical_forecasts)
 import(Rcpp)
 importFrom(dplyr,"%>%")
diff --git a/R/R/metrics.R b/R/R/metrics.R
new file mode 100644
index 0000000..ce9a5ce
--- /dev/null
+++ b/R/R/metrics.R
@@ -0,0 +1,150 @@
+## Copyright (c) 2017-present, Facebook, Inc.
+## All rights reserved.
+
+## This source code is licensed under the BSD-style license found in the
+## LICENSE file in the root directory of this source tree. An additional grant
+## of patent rights can be found in the PATENTS file in the same directory.
+
+#' @title Metrics for Time Series Forecasts
+#'
+#' @description
+#' A time-series forecast requires making a quantitative prediction of future values.
+#' After forecast, we also have to provide accurracy of forecasts to check wether the forecast serves our need.
+#' Metrics for time series forecasts are so useful in telling you how your model is good and helping you determine which particular forecasting models work best.
+#'
+#' @details
+#' Here, as a notation, we assume that \eqn{y} is the actual value and \eqn{yhat} is the forecast value.
+#'
+#' Mean Error (ME, \code{me})
+#'
+#' The Mean Error (ME)  is defined by the formula:
+#' \deqn{ \frac{1}{n} \sum_{t=1}^{n} y_{t}-yhat_{t} .}
+#'
+#' Mean Squared Error (MSE, \code{mse})
+#'
+#' The Mean Squared Error (MSE)  is defined by the formula:
+#' \deqn{ \frac{1}{n} \sum_{t=1}^{n} (y_{t}-yhat_{t})^2 .}
+#'
+#' Root Mean Square Error (RMSE, \code{rmse})
+#'
+#' Root Mean Square Error (RMSE) is define by the formula:
+#' \deqn{ \sqrt{\frac{1}{n} \sum_{t=1}^{n} (y_{t}-yhat_{t})^2} .}
+#'
+#' Mean Absolute Error (MAE, \code{mae})
+#'
+#' The Mean Absolute Error (MAE) is defined by the formula:
+#' \deqn{ \frac{1}{n} \sum_{t=1}^{n} | y_{t}-yhat_{t} | .}
+#'
+#' Mean Percentage Error (MPE, \code{mpe})
+#'
+#' The Mean Percentage Error (MPE) is usually expressed as a percentage
+#' and is defined by the formula:
+#' \deqn{ \frac{100}{n} \sum_{t=1}^{n} \frac {y_{t}-yhat_{t}}{y_{t}} .}
+#'
+#' Mean Absolute Percentage Error (MAPE, \code{mape})
+#'
+#' The Mean absolute Percentage Error (MAPE), also known as Mean Absolute Percentage Deviation (MAPD), is usually expressed as a percentage,
+#' and is defined by the formula:
+#' \deqn{ \frac{100}{n} \sum_{t=1}^{n} | \frac {y_{t}-yhat_{t}}{y_{t}}| .}
+#'
+#' @param m Prophet object. Default NULL
+#' @param df A dataframe which is output of `simulated_historical_forecasts` or `cross_validation` Default NULL
+#'
+#' @return metrics value (numeric)
+#'
+#'@examples
+#'\dontrun{
+#' # Create example model
+#' library(readr)
+#' library(prophet)
+#' df <- read_csv('../tests/testthat/data.csv')
+#' m <- prophet(df)
+#' future <- make_future_dataframe(m, periods = 365)
+#' forecast <- predict(m, future)
+#' all_metrics(forecast)
+#' df.cv <- cross_validation(m, horizon = 100, units = 'days')
+#' all_metrics(df.cv)
+#' # You can check your models's accuracy using me, mse, rmse ...etc.
+#' print(rmse(m))
+#'}
+#' @name metrics
+NULL
+
+#' Prepare dataframe for metrics calculation.
+#'
+#' @param m Prophet object. Default NULL
+#' @param df A dataframe which is output of `simulated_historical_forecasts` or `cross_validation` Default NULL
+#'
+#' @return A dataframe only with y and yhat as a column.
+#'
+#' @keywords internal
+create_metric_data <- function(m=NULL, df=NULL)
+{
+  if(is.null(m) && is.null(df))
+  {
+    stop("You have to specify one of `m` and `df` at least.")
+  }
+  if(!is.null(m) && !is.null(df))
+  {
+    warning("You specify both of `m` and `df`. `df` is used for metrics calclation.")
+  }
+
+  data <- if(!is.null(df)){
+    df
+  } else if("prophet" %in% class(m)) {
+    dplyr::inner_join(m$history, predict(m, NULL), by="ds")
+  }
+
+  dplyr::select(data, y, yhat) %>% na.omit()
+}
+
+#' Meta function to make the function which evaluate metrics.
+#'
+#' @param metrics metrics function
+#'
+#' @return A function using for metrics evaluation.
+#'
+#' @keywords internal
+make_metrics_function <- function(metrics)
+{
+  function(m=NULL, df=NULL)
+  {
+    data <- create_metric_data(m, df)
+    metrics(data$y, data$yhat)
+  }
+}
+
+#' @rdname metrics
+#' @export
+me <- make_metrics_function(function(y, yhat){mean(y - yhat)})
+
+#' @rdname metrics
+#' @export
+mse <- make_metrics_function(function(y, yhat){mean((y - yhat)^2)})
+
+#' @rdname metrics
+#' @export
+rmse <- make_metrics_function(function(y, yhat){sqrt(mean((y - yhat)^2))})
+
+#' @rdname metrics
+#' @export
+mae <- make_metrics_function(function(y, yhat){mean(abs(y - yhat))})
+
+#' @rdname metrics
+#' @export
+mpe <- make_metrics_function(function(y, yhat){100*mean((y - yhat)/y)})
+
+#' @rdname metrics
+#' @export
+mape <- make_metrics_function(function(y, yhat){100*mean(abs((y - yhat)/y))})
+
+#' @rdname metrics
+#' @export
+all_metrics <- function(m=NULL, df=NULL)
+{
+  # Define all metrics functions as a character
+  metrics <- rlang::set_names(c("me", "mse", "rmse", "mae", "mpe", "mape"))
+  # Convert character to function and evalate each metrics in invoke_map_df
+  # The result is data.frame with each metrics name
+  purrr::invoke_map_df(metrics, list(list(m, df)))
+}
diff --git a/R/man/create_metric_data.Rd b/R/man/create_metric_data.Rd
new file mode 100644
index 0000000..6dc5e9d
--- /dev/null
+++ b/R/man/create_metric_data.Rd
@@ -0,0 +1,18 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/metrics.R
+\name{create_metric_data}
+\alias{create_metric_data}
+\title{Prepare dataframe for metrics calculation.}
+\usage{
+create_metric_data(fcst)
+}
+\arguments{
+\item{fcst}{Dataframe output of `predict`.}
+}
+\value{
+A dataframe only with y and yhat as a column.
+}
+\description{
+Prepare dataframe for metrics calculation.
+}
+\keyword{internal}
diff --git a/R/man/metrics.Rd b/R/man/metrics.Rd
new file mode 100644
index 0000000..96d74c2
--- /dev/null
+++ b/R/man/metrics.Rd
@@ -0,0 +1,83 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/metrics.R
+\name{metrics}
+\alias{metrics}
+\alias{me}
+\alias{mse}
+\alias{rmse}
+\alias{mae}
+\alias{mpe}
+\alias{mape}
+\alias{all_metrics}
+\title{Metrics for Time Series Forecasts}
+\usage{
+me(fcst)
+
+mse(fcst)
+
+rmse(fcst)
+
+mae(fcst)
+
+mpe(fcst)
+
+mape(fcst)
+
+all_metrics(fcst)
+}
+\arguments{
+\item{fcst}{Dataframe output of `predict`.}
+}
+\value{
+metrics value (numeric)
+}
+\description{
+A time-series forecast requires making a quantitative prediction of future values.
+After forecast, we also have to provide accurracy of forecasts to check wether the forecast serves our need.
+Metrics for time series forecasts are so useful in telling you how your model is good and helping you determine which particular forecasting models work best.
+}
+\details{
+Here, as a notation, we assume that \eqn{y} is the actual value and \eqn{yhat} is the forecast value.
+
+Mean Error (ME, \code{me})
+
+The Mean Error (ME)  is defined by the formula:
+\deqn{ \frac{1}{n} \sum_{t=1}^{n} y_{t}-yhat_{t} .}
+
+Mean Squared Error (MSE, \code{mse})
+
+The Mean Squared Error (MSE)  is defined by the formula:
+\deqn{ \frac{1}{n} \sum_{t=1}^{n} (y_{t}-yhat_{t})^2 .}
+
+Root Mean Square Error (RMSE, \code{rmse})
+
+Root Mean Square Error (RMSE) is define by the formula:
+\deqn{ \sqrt{\frac{1}{n} \sum_{t=1}^{n} (y_{t}-yhat_{t})^2} .}
+
+Mean Absolute Error (MAE, \code{mae})
+
+The Mean Absolute Error (MAE) is defined by the formula:
+\deqn{ \frac{1}{n} \sum_{t=1}^{n} | y_{t}-yhat_{t} | .}
+
+Mean Percentage Error (MPE, \code{mpe})
+
+The Mean Percentage Error (MPE) is usually expressed as a percentage
+and is defined by the formula:
+\deqn{ \frac{100}{n} \sum_{t=1}^{n} \frac {y_{t}-yhat_{t}}{y_{t}} .}
+
+Mean Absolute Percentage Error (MAPE, \code{mape})
+
+The Mean absolute Percentage Error (MAPE), also known as Mean Absolute Percentage Deviation (MAPD), is usually expressed as a percentage,
+and is defined by the formula:
+\deqn{ \frac{100}{n} \sum_{t=1}^{n} | \frac {y_{t}-yhat_{t}}{y_{t}}| .}
+}
+\examples{
+\dontrun{
+# Create example model
+library(readr)
+df <- read_csv('../tests/testthat/data.csv')
+m <- prophet(df)
+# You can check your models's accuracy using me, mse, rmse ...etc.
+print(rmse(m))
+}
+}
diff --git a/R/tests/testthat/test_metrics.R b/R/tests/testthat/test_metrics.R
new file mode 100644
index 0000000..a592ed2
--- /dev/null
+++ b/R/tests/testthat/test_metrics.R
@@ -0,0 +1,60 @@
+library(prophet)
+context("Prophet metrics tests")
+
+## Makes R CMD CHECK happy due to dplyr syntax below
+globalVariables(c("y", "yhat"))
+
+DATA <- head(read.csv('data.csv'), 100)
+DATA$ds <- as.Date(DATA$ds)
+
+test_that("metrics_tests_using_model", {
+  # Create dummy model
+  m <- prophet(DATA)
+  # Create metric data
+  forecast <- predict(m, NULL)
+  df <- na.omit(dplyr::inner_join(m$history, forecast, by="ds"))
+  # Check all metrics wether it is equal to its definition
+  y <- df$y
+  yhat <- df$yhat
+  expect_equal(me(m), mean(y-yhat))
+  expect_equal(mse(m), mean((y-yhat)^2))
+  expect_equal(rmse(m), sqrt(mean((y-yhat)^2)))
+  expect_equal(mae(m), mean(abs(y-yhat)))
+  expect_equal(mpe(m), 100*mean((y-yhat)/y))
+  expect_equal(mape(m), 100*mean(abs((y-yhat)/y)))
+  answer <- data.frame(
+    me=me(m),
+    mse=mse(m),
+    rmse=rmse(m),
+    mae=mae(m),
+    mpe=mpe(m),
+    mape=mape(m)
+  )
+  expect_equal(all_metrics(m), answer)
+})
+
+test_that("metrics_tests_using_simulated_historical_forecast", {
+  #skip_if_not(Sys.getenv('R_ARCH') != '/i386')
+  # Create dummy model
+  m <- prophet(DATA)
+  # Run simulated historical forecast
+  df <- simulated_historical_forecasts(m, horizon = 3, units = 'days', k = 2, period = 3)
+  # Check all metrics wether it is equal to its definition
+  y <- df$y
+  yhat <- df$yhat
+  expect_equal(me(df=df), mean(y-yhat))
+  expect_equal(mse(df=df), mean((y-yhat)^2))
+  expect_equal(rmse(df=df), sqrt(mean((y-yhat)^2)))
+  expect_equal(mae(df=df), mean(abs(y-yhat)))
+  expect_equal(mpe(df=df), 100*mean((y-yhat)/y))
+  expect_equal(mape(df=df), 100*mean(abs((y-yhat)/y)))
+  answer <- data.frame(
+    me=me(df=df),
+    mse=mse(df=df),
+    rmse=rmse(df=df),
+    mae=mae(df=df),
+    mpe=mpe(df=df),
+    mape=mape(df=df)
+  )
+  expect_equal(all_metrics(df=df), answer)
+})