Prophet + BoxCox for arima and ets

1. Результаты прогнозирования с использованием пакета prophet

# View first 20 rows of the forecasting result using prophet package on M3-competition data
# ds: timestamp
# yhat: forecasts
# yhat_lower: Lo 80
# yhat_upper: Hi 80
# yhat_lower95: Lo 95
# yhat_upper95: Hi 95
head(M3Prophet, 20)

Использование пакета pcbars для оценки этого результата

# View total data on each horizon
M3Prophet_total

Для forecast_within_80:

# Using pcbars package for M3Prophet_total data
# Input: A data frame containing columns: category, case and total
# For forecasts within 80%
category <- paste("Prophet_with_horizon =", unique(M3Prophet_total$horizon))
cases <- M3Prophet_total$forecast_within_80
total <- M3Prophet_total$total_forecast
df1 <- data.frame(category, cases, total)
estimatePercentageErrors(df1)

Для forecast_within_95

# Using pcbars package for M3Prophet_total data
# Input: A data frame containing columns: category, case and total
# For forecasts within 95%
category <- paste("Prophet_with_horizon =", unique(M3Prophet_total$horizon))
cases <- M3Prophet_total$forecast_within_95
total <- M3Prophet_total$total_forecast
df2 <- data.frame(category, cases, total)
estimatePercentageErrors(df2)

Анализ в разрезе типов рядов

# View the M3prophet_total_category
M3prophet_total_category

Для forecast_within_80:

# pcbars for M3prophet_total_category data
# input: A data frame containing columns category, case, total
# for forecasts within 80 %
# conf. interval of estimatePercentageErrors by default: 95%
category <- M3prophet_total_category$category
cases <- M3prophet_total_category$forecast_within_80
total <- M3prophet_total_category$total_forecast
df3 <- data.frame(category, cases, total)
estimatePercentageErrors(df3)

Для forecast_within_95:

# pcbars for M3prophet_total_category data
# input: A data frame containing columns category, case, total
# for forecasts within 95 %
# conf. interval of estimatePercentageErrors by default: 95%
category <- M3prophet_total_category$category
cases <- M3prophet_total_category$forecast_within_95
total <- M3prophet_total_category$total_forecast
df4 <- data.frame(category, cases, total)
estimatePercentageErrors(df4)

2. Результаты прогнозирования с использованием Arima и BoxCox преобразованием

# View first 20 rows of M3Arima_BoxCox data
head(M3Arima_BoxCox, 20)

набор данных, полученный с применением BoxCox и Arima имеет NAs (missing values) в колонках Hi80 and Hi95

# Check whether rows contain any NAs:
row.has.na <- apply(M3Arima_BoxCox, 1, function(x){any(is.na(x))})
sum(row.has.na)

[1] 5048

# Remove NAs values
df5 <- M3Arima_BoxCox[complete.cases(M3Arima_BoxCox), ]

Использование пакета pcbars для оценки этого результата после удаления строк с NAs values

# View M3Arima_total_BoxCox data 
M3Arima_total_BoxCox

Для forecast_within_80:

# Using pcbars package for M3Arima_total_BoxCox data
# Input: A data frame containing columns: category, case and total
# For forecasts within 80%
category <- paste("Arima-Boxcox_with_horizon =", unique(M3Arima_total_BoxCox$horizon))
cases <- M3Arima_total_BoxCox$forecast_within_80
total <- M3Arima_total_BoxCox$total_forecast
df6 <- data.frame(category, cases, total)
estimatePercentageErrors(df6)

Для forecast_within_95:

# Using pcbars package for M3Arima_total_BoxCox data
# Input: A data frame containing columns: category, case and total
# For forecasts within 95%
category <- paste("Arima-Boxcox_with_horizon =", unique(M3Arima_total_BoxCox$horizon))
cases <- M3Arima_total_BoxCox$forecast_within_95
total <- M3Arima_total_BoxCox$total_forecast
df7 <- data.frame(category, cases, total)
estimatePercentageErrors(df7)

Анализ в разрезе типов рядов

# View the M3Arima_total_BoxCox_category data
M3Arima_total_BoxCox_category

Для forecast_within_80:

# pcbars for M3Arima_total_BoxCox_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_BoxCox_category$category
cases <- M3Arima_total_BoxCox_category$forecast_within_80
total <- M3Arima_total_BoxCox_category$total_forecast
df8 <- data.frame(category, cases, total)
estimatePercentageErrors(df8)

Для forecast_within_95:

# pcbars for M3Arima_total_BoxCox_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_BoxCox_category$category
cases <- M3Arima_total_BoxCox_category$forecast_within_95
total <- M3Arima_total_BoxCox_category$total_forecast
df9 <- data.frame(category, cases, total)
estimatePercentageErrors(df9)

3. Результаты прогнозирования с использованием Ets and BoxCox преобразования:

# View first 20 rows of M3Ets_BoxCox data
head(M3Ets_BoxCox, 20)

набор данных, полученный с применением ETS и BoxCox имеет NAs (missing values) в колонках Hi80 and Hi95

# Check whether rows contain any NAs:
row.has.na <- apply(M3Ets_BoxCox, 1, function(x){any(is.na(x))})
sum(row.has.na)

[1] 378

# Remove NAs values
df10<- M3Ets_BoxCox[complete.cases(M3Ets_BoxCox), ]

Использование пакета pcbars для оценки этого результата после удаления строк с NAs values

# View M3Arima_total_BoxCox data 
M3Ets_total_BoxCox

Для forecast_within_80:

# Using pcbars package for M3Ets_total_BoxCox data
# Input: A data frame containing columns: category, case and total
# For forecasts within 80%
category <- paste("ETS-Boxcox_with_horizon =", unique(M3Ets_total_BoxCox$horizon))
cases <- M3Ets_total_BoxCox$forecast_within_80
total <- M3Ets_total_BoxCox$total_forecast
df11<- data.frame(category, cases, total)
estimatePercentageErrors(df11)

Для forecast_within_95:

# Using pcbars package for M3Ets_total_BoxCox data
# Input: A data frame containing columns: category, case and total
# For forecasts within 95%
category <- paste("ETS-Boxcox_with_horizon =", unique(M3Ets_total_BoxCox$horizon))
cases <- M3Ets_total_BoxCox$forecast_within_95
total <- M3Ets_total_BoxCox$total_forecast
df12<- data.frame(category, cases, total)
estimatePercentageErrors(df12)

Анализ в разрезе типов рядов

# View the M3prophet_total_category
M3Ets_total_BoxCox_category

Для forecast_within_80:

# pcbars for M3Ets_total_BoxCox_category data
# input: A data frame containing columns category, case, total
# for forecasts within 80 %
# conf. interval of estimatePercentageErrors by default: 95%
category <- M3Ets_total_BoxCox_category$category
cases <- M3Ets_total_BoxCox_category$forecast_within_80
total <- M3Ets_total_BoxCox_category$total_forecast
df13<- data.frame(category, cases, total)
estimatePercentageErrors(df13)

Для forecast_within_95:

# pcbars for M3Ets_total_BoxCox_category data
# input: A data frame containing columns category, case, total
# for forecasts within 95 %
# conf. interval of estimatePercentageErrors by default: 95%
category <- M3Ets_total_BoxCox_category$category
cases <- M3Ets_total_BoxCox_category$forecast_within_95
total <- M3Ets_total_BoxCox_category$total_forecast
df14<- data.frame(category, cases, total)
estimatePercentageErrors(df14)