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91 lines
2.6 KiB
R
91 lines
2.6 KiB
R
% Generated by roxygen2: do not edit by hand
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% Please edit documentation in R/metrics.R
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\name{metrics}
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\alias{metrics}
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\alias{me}
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\alias{mse}
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\alias{rmse}
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\alias{mae}
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\alias{mpe}
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\alias{mape}
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\alias{all_metrics}
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\title{Metrics for Time Series Forecasts}
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\usage{
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me(m = NULL, df = NULL)
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mse(m = NULL, df = NULL)
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rmse(m = NULL, df = NULL)
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mae(m = NULL, df = NULL)
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mpe(m = NULL, df = NULL)
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mape(m = NULL, df = NULL)
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all_metrics(m = NULL, df = NULL)
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}
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\arguments{
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\item{m}{Prophet object. Default NULL}
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\item{df}{A dataframe which is output of `simulated_historical_forecasts` or `cross_validation` Default NULL}
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}
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\value{
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metrics value (numeric)
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}
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\description{
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A time-series forecast requires making a quantitative prediction of future values.
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After forecast, we also have to provide accurracy of forecasts to check wether the forecast serves our need.
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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.
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}
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\details{
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Here, as a notation, we assume that \eqn{y} is the actual value and \eqn{yhat} is the forecast value.
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Mean Error (ME, \code{me})
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The Mean Error (ME) is defined by the formula:
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\deqn{ \frac{1}{n} \sum_{t=1}^{n} y_{t}-yhat_{t} .}
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Mean Squared Error (MSE, \code{mse})
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The Mean Squared Error (MSE) is defined by the formula:
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\deqn{ \frac{1}{n} \sum_{t=1}^{n} (y_{t}-yhat_{t})^2 .}
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Root Mean Square Error (RMSE, \code{rmse})
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Root Mean Square Error (RMSE) is define by the formula:
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\deqn{ \sqrt{\frac{1}{n} \sum_{t=1}^{n} (y_{t}-yhat_{t})^2} .}
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Mean Absolute Error (MAE, \code{mae})
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The Mean Absolute Error (MAE) is defined by the formula:
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\deqn{ \frac{1}{n} \sum_{t=1}^{n} | y_{t}-yhat_{t} | .}
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Mean Percentage Error (MPE, \code{mpe})
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The Mean Percentage Error (MPE) is usually expressed as a percentage
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and is defined by the formula:
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\deqn{ \frac{100}{n} \sum_{t=1}^{n} \frac {y_{t}-yhat_{t}}{y_{t}} .}
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Mean Absolute Percentage Error (MAPE, \code{mape})
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The Mean absolute Percentage Error (MAPE), also known as Mean Absolute Percentage Deviation (MAPD), is usually expressed as a percentage,
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and is defined by the formula:
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\deqn{ \frac{100}{n} \sum_{t=1}^{n} | \frac {y_{t}-yhat_{t}}{y_{t}}| .}
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}
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\examples{
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\dontrun{
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# Create example model
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library(readr)
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library(prophet)
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df <- read_csv('../tests/testthat/data.csv')
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m <- prophet(df)
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future <- make_future_dataframe(m, periods = 365)
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forecast <- predict(m, future)
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all_metrics(forecast)
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df.cv <- cross_validation(m, horizon = 100, units = 'days')
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all_metrics(df.cv)
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# You can check your models's accuracy using me, mse, rmse ...etc.
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print(rmse(m))
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}
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}
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