# Exploring forecast

Let’s examine some of the functions inside for forecast

Michael DeWitt https://michaeldewittjr.com
07-07-2018

Time series forecasting is interesting. I want to write more, but not right now. I’m just going to lay out a few functions for explanation later.

``````
library(fpp2)
library(purrr)
library(dplyr)``````

I am going to test a few different methods:

• Mean forecasting

• Random Walk (last value is forecast with an addition)

• Seasonal Naive (last seasons value is next guess)

• Naive (Previous value is forecast)

``````
beer2 <- window(ausbeer, start= 1992, end = c(2007,4))

beer_fit1 <- meanf(beer2, h=10)
beer_fit2 <- rwf(beer2, h=10)
beer_fit3 <- snaive(beer2, h=10)
beer_fit4 <- naive(beer2, h=10)``````

Let’s see how we did on the forecast.

``````
beer3 <- window(ausbeer, start =2008)

models <- list(mean_forecast = beer_fit1, rand_walk = beer_fit2, seasonal_naive = beer_fit3,
naive = beer_fit4)
map(models, accuracy, beer3)``````
``````
\$mean_forecast
ME     RMSE      MAE        MPE     MAPE     MASE
Training set   0.000 43.62858 35.23438 -0.9365102 7.886776 2.463942
Test set     -13.775 38.44724 34.82500 -3.9698659 8.283390 2.435315
ACF1 Theil's U
Training set -0.10915105        NA
Test set     -0.06905715  0.801254

\$rand_walk
ME     RMSE      MAE         MPE     MAPE
Training set   0.4761905 65.31511 54.73016  -0.9162496 12.16415
Test set     -51.4000000 62.69290 57.40000 -12.9549160 14.18442
MASE        ACF1 Theil's U
Training set 3.827284 -0.24098292        NA
Test set     4.013986 -0.06905715  1.254009

\$seasonal_naive
ME     RMSE  MAE        MPE     MAPE      MASE
Training set -2.133333 16.78193 14.3 -0.5537713 3.313685 1.0000000
Test set      5.200000 14.31084 13.4  1.1475536 3.168503 0.9370629
ACF1 Theil's U
Training set -0.2876333        NA
Test set      0.1318407  0.298728

\$naive
ME     RMSE      MAE         MPE     MAPE
Training set   0.4761905 65.31511 54.73016  -0.9162496 12.16415
Test set     -51.4000000 62.69290 57.40000 -12.9549160 14.18442
MASE        ACF1 Theil's U
Training set 3.827284 -0.24098292        NA
Test set     4.013986 -0.06905715  1.254009``````

Seasonal Naive looks like it won!

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Text and figures are licensed under Creative Commons Attribution CC BY 4.0. Source code is available at https://github.com/medewitt/medewitt.github.io, unless otherwise noted. The figures that have been reused from other sources don't fall under this license and can be recognized by a note in their caption: "Figure from ...".

### Citation

`DeWitt (2018, July 7). Michael DeWitt: Exploring forecast. Retrieved from https://michaeldewittjr.com/programming/2018-07-08-exploring-forecast/`
```@misc{dewitt2018exploring,