library(TSstudio)
data(USgas)
ts_info(USgas)
## The USgas series is a ts object with 1 variable and 238 observations
## Frequency: 12
## Start time: 2000 1
## End time: 2019 10
train <- window(USgas,
start = time(USgas)[1],
end = time(USgas)[length(USgas) - 12])
test <- window(USgas,
start = time(USgas)[length(USgas) - 12 + 1],
end = time(USgas)[length(USgas)])
ts_info(train)
## The train series is a ts object with 1 variable and 226 observations
## Frequency: 12
## Start time: 2000 1
## End time: 2018 10
ts_info(test)
## The test series is a ts object with 1 variable and 12 observations
## Frequency: 12
## Start time: 2018 11
## End time: 2019 10
USgas_partitions <- ts_split(USgas, sample.out = 12)
train <- USgas_partitions$train
test <- USgas_partitions$test
ts_info(train)
## The train series is a ts object with 1 variable and 226 observations
## Frequency: 12
## Start time: 2000 1
## End time: 2018 10
ts_info(test)
## The test series is a ts object with 1 variable and 12 observations
## Frequency: 12
## Start time: 2018 11
## End time: 2019 10
library(forecast)
## Registered S3 method overwritten by 'quantmod':
## method from
## as.zoo.data.frame zoo
md <- auto.arima(train)
checkresiduals(md)
##
## Ljung-Box test
##
## data: Residuals from ARIMA(2,1,1)(2,1,1)[12]
## Q* = 24.949, df = 18, p-value = 0.1263
##
## Model df: 6. Total lags used: 24
fc <- forecast(md, h = 12)
accuracy(fc, test)
## ME RMSE MAE MPE MAPE MASE
## Training set 5.843706 97.81628 73.42676 0.1170431 3.522362 0.6376877
## Test set 37.838606 103.22567 81.46281 1.3104256 3.261542 0.7074783
## ACF1 Theil's U
## Training set -0.004164654 NA
## Test set -0.046706738 0.340398
test_forecast(actual = USgas,
forecast.obj = fc,
test = test)
library(forecast)
naive_model <- naive(train, h = 12)
test_forecast(actual = USgas,
forecast.obj = naive_model,
test = test)
accuracy(naive_model, test)
## ME RMSE MAE MPE MAPE MASE
## Training set -1.028444 285.6607 228.5084 -0.9218463 10.97123 1.984522
## Test set 301.891667 499.6914 379.1417 9.6798015 13.28187 3.292723
## ACF1 Theil's U
## Training set 0.3761105 NA
## Test set 0.7002486 1.499679
snaive_model <- snaive(train, h = 12)
test_forecast(actual = USgas,
forecast.obj = snaive_model,
test = test)
accuracy(snaive_model, test)
## ME RMSE MAE MPE MAPE MASE ACF1
## Training set 33.99953 148.7049 115.1453 1.379869 5.494048 1.000000 0.4859501
## Test set 96.45000 164.6967 135.8833 3.612060 5.220458 1.180103 -0.2120929
## Theil's U
## Training set NA
## Test set 0.4289964
md_final <- auto.arima(USgas)
fc_final <- forecast(md_final, h = 12)
plot_forecast(fc_final,
title = "The US Natural Gas Consumption Forecast",
Xtitle = "Year",
Ytitle = "Billion Cubic Feet")
fc_final2 <- forecast(md_final,
h = 60,
level = c(80, 90))
plot_forecast(fc_final2,
title = "The US Natural Gas Consumption Forecast",
Xtitle = "Year",
Ytitle = "Billion Cubic Feet")
fc_final3 <- forecast_sim(model = md_final,
h = 60,
n = 500)
library(plotly)
## Loading required package: ggplot2
##
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
##
## last_plot
## The following object is masked from 'package:stats':
##
## filter
## The following object is masked from 'package:graphics':
##
## layout
fc_final3$plot %>%
layout(title = "US Natural Gas Consumption - Forecasting Simulation",
yaxis = list(title = "Billion Cubic Feet"),
xaxis = list(title = "Year"))