M3total_from_ets

# View the M3EtsTotaldata
M3EtsTotal

# pcbars for M3EtsTotal data
# input: A data frame containing columns category, case, total
# for forecasts within 95 %
# conf. interval of estimatePercentageErrors by default: 95%
category <- paste("ETS_with_horizon =", unique(M3EtsTotal$horizon))
cases <- M3EtsTotal$forecasts_within_95
total <- M3EtsTotal$total_forecast
df1 <- data.frame(category, cases, total)
estimatePercentageErrors(df1)

# pcbars for M3total data
# input: A data frame containing columns category, case, total
# for forecasts within 80 %
# conf. interval of estimatePercentageErrors  by default: 95%
category <- paste("ETS_with_horizon =", unique(M3EtsTotal$horizon))
cases <- M3EtsTotal$forecasts_within_80
total <- M3EtsTotal$total_forecast
df2 <- data.frame(category, cases, total)
estimatePercentageErrors(df2)

category <- c("forecast within 80%", "forecast within 95%")
cases <- c(sum(M3EtsTotal$forecasts_within_80), sum(M3EtsTotal$forecasts_within_95))
total <- sum(M3EtsTotal$total_forecast)
df3 <- data.frame(category, cases, total)
estimatePercentageErrors(df3, conf.level = 0.8)

Анализ в разрезе типов рядов

# View the M3Arima_total_category data
M3Arima_total_category

# pcbars for M3Arima_total_category data
# input: A data frame containing columns category, case, total
# for forecasts within 95 %
# conf. interval of estimatePercentageErrors by default: 95%
category <- M3Arima_total_category$category
cases <- M3Arima_total_category$forecasts_within_95
total <- M3Arima_total_category$total_forecast
df4 <- data.frame(category, cases, total)
estimatePercentageErrors(df4)

# pcbars for M3Arima_total_category data
# input: A data frame containing columns category, case, total
# for forecasts within 80 %
# conf. interval of estimatePercentageErrors by default: 95%
category <- M3Arima_total_category$category
cases <- M3Arima_total_category$forecasts_within_80
total <- M3Arima_total_category$total_forecast
df5 <- data.frame(category, cases, total)
estimatePercentageErrors(df5)