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1hrs_pv1_report_generator.R
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source('funcs.R')
# benchmark models ----
report.full(model = 'snaive()',
series = '1hrs pv1',
transformation = 'identity()',
traindays = 7,
testdays = 1)
report.full(model = 'meanf()',
series = '1hrs pv1',
transformation = 'identity()',
traindays = 7,
testdays = 1)
report.full(model = 'naive()',
series = '1hrs pv1',
transformation = 'identity()',
traindays = 7,
testdays = 1)
# try to find the best ARIMA model ----
report(model = 'Arima(order=c(1, 0, 0))',
series = '1hrs pv1',
transformation = 'identity()',
diffs = 'identity()',
sdiffs = 'identity()',
startday = 0,
traindays = 7,
testdays = 3)
report(model = 'Arima(order=c(2, 0, 0))',
series = '1hrs pv1',
transformation = 'identity()',
diffs = 'identity()',
sdiffs = 'identity()',
startday = 0,
traindays = 7,
testdays = 3)
report(model = 'Arima(order=c(3, 0, 0))',
series = '1hrs pv1',
transformation = 'identity()',
diffs = 'identity()',
sdiffs = 'identity()',
startday = 0,
traindays = 7,
testdays = 3)
report(model = 'Arima(order=c(1, 0, 0), seasonal=c(1, 0, 0))',
series = '1hrs pv1',
transformation = 'identity()',
diffs = 'identity()',
sdiffs = 'identity()',
startday = 0,
traindays = 7,
testdays = 3)
# add fourier terms
report(model = 'Arima(order=c(1, 0, 0), seasonal=c(1, 0, 0), xreg=fourier(., K=2))',
series = '1hrs pv1',
transformation = 'identity()',
diffs = 'identity()',
sdiffs = 'identity()',
startday = 0,
traindays = 7,
testdays = 3,
xreg='fourier(., K=2, h=h)')
# Find the best train:test days ratio for ARIMA(1,0,0)(1,0,0) ----
scaler <- 1/100000000000
best.fcast.1hrsPv1 <- NULL
best.traindays <- 0
best.testdays <- 0
for(traindays in 3:7)
{
for(testdays in 2:3)
{
print(paste("Trying", traindays, "train days and", testdays, "test days"))
current <- fullforecast(model = 'Arima(order=c(1, 0, 0), seasonal=c(1, 0, 0), method="CSS")',
dataset = datasets[['1hrs pv1']]$series * scaler,
transformation = 'identity()',
traindays = traindays,
testdays = testdays,
xreg=NULL)
if(is.null(best.fcast.1hrsPv1) || current$accuracy[[2]] < best.fcast.1hrsPv1$accuracy[[2]])
{
best.fcast.1hrsPv1 <- current
best.traindays <- traindays
best.testdays <- testdays
}
}
}
report.full(model = 'Arima(order=c(1, 0, 0), seasonal=c(1, 0, 0), method="ML")',
series = '1hrs pv1',
transformation = 'identity()',
traindays = best.traindays, # 5
testdays = best.testdays) # 2
# Skip over the step where I hardcode a fourier value ----
# Find best K for the above model ARIMA(1,0,0)(1,0,0) ----
best.fcast.k.1hrsPv1 <- NULL
best.k <- 0
#K must be not be greater than period/2
for(k in 1:(frequency(datasets[['1hrs pv1']]$series)/2))
{
print(paste("Trying k =", k))
m <- paste0('Arima(order=c(1, 0, 0), seasonal=c(1, 0, 0), method="CSS", xreg=fourier(., K=', k, '))')
xreg <- paste0('fourier(., h=h, K=', k, ')')
current <- fullforecast(model = m,
dataset = datasets[['1hrs pv1']]$series * scaler,
transformation = 'identity()',
traindays = best.traindays, # ?
testdays = best.testdays, # ?
xreg=xreg)
if(is.null(best.fcast.k.1hrsPv1) || current$accuracy[[2]] < best.fcast.k.1hrsPv1$accuracy[[2]])
{
best.fcast.k.1hrsPv1 <- current
best.k <- k
}
}
report.full(model = paste('Arima(order=c(1, 0, 0), seasonal=c(1, 0, 0), method="CSS", xreg=fourier(., K=', best.k, '))', sep=''),
series = '1hrs pv1',
transformation = 'identity()',
traindays = best.traindays, # 5
testdays = best.testdays, # 2
xreg = paste('fourier(., h=h, K=', best.k, ')')) # 2
# Best model: 5:2, ARIMA(1, 0, 0)(1, 0, 0), K=2, RMSE=460 MAE=232 ----
# 11th-17th obs dummies rmse=461, mae=230
# without seasonal part, only fourier: rmse=460, mae=234
# without seasonal part, 2AR, only fourier: rmse=460, mae=234
# without seasonal part, 3AR, only fourier: rmse=460, mae=234
# dummies without seasonal part, only fourier: rmse=460, mae=237
# dummies without seasonal part, 2AR, only fourier: rmse=460, mae=237
# dummies without seasonal part, 3AR, only fourier: rmse=460, mae=237
report.full(model = 'Arima(order=c(1, 0, 0), seasonal=c(1, 0, 0), method="CSS", xreg=fourier(., K=2))',
series = '1hrs pv1',
transformation = 'identity()',
traindays = 5,
testdays = 2,
xreg = 'fourier(., h=h, K=2)')
dummies.fcast <- quote(
{cbind(
dummies=getNthObsDummies(11, 6, h, frequency(.)),
fourier(., h=h, K=2)
)}
)
dummies.fit <- quote(
{cbind(
dummies=getNthObsDummies(11, 6, length(.), frequency(.)),
fourier(., K=2)
)}
)
report.full(model = paste0('Arima(order=c(1, 0, 0), seasonal=c(1, 0, 0), method="CSS", xreg=', paste0(deparse(dummies.fit), collapse='') ,')'),
series = '1hrs pv1',
transformation = 'identity()',
traindays = 5,
testdays = 2,
xreg = paste0(deparse(dummies.fcast), collapse=''))
# NO SAR, with dummies
# rmse=460, mae=234
report.full(model = paste0('Arima(order=c(1, 0, 0), method="CSS", xreg=', paste0(deparse(dummies.fit), collapse='') ,')'),
series = '1hrs pv1',
transformation = 'identity()',
traindays = 5,
testdays = 2,
xreg = paste0(deparse(dummies.fcast), collapse=''))
# no SAR with dummies
# 2 AR terms
# rmse=460, mae=234
report.full(model = paste0('Arima(order=c(2, 0, 0), method="CSS", xreg=', paste0(deparse(dummies.fit), collapse='') ,')'),
series = '1hrs pv1',
transformation = 'identity()',
traindays = 5,
testdays = 2,
xreg = paste0(deparse(dummies.fcast), collapse=''))
# no SAR with dummies
# 3 AR terms
# rmse=, mae=
report.full(model = paste0('Arima(order=c(3, 0, 0), method="CSS", xreg=', paste0(deparse(dummies.fit), collapse='') ,')'),
series = '1hrs pv1',
transformation = 'identity()',
traindays = 5,
testdays = 2,
xreg = paste0(deparse(dummies.fcast), collapse=''))
# no SAR, no dummies
# rmse=460, mae=237
report.full(model = 'Arima(order=c(1, 0, 0), method="CSS", xreg=fourier(., K=2))',
series = '1hrs pv1',
transformation = 'identity()',
traindays = 5,
testdays = 2,
xreg = 'fourier(., h=h, K=2)')
# no SAR, no dummies, 2AR
# rmse=460, mae=237
report.full(model = 'Arima(order=c(2, 0, 0), method="CSS", xreg=fourier(., K=2))',
series = '1hrs pv1',
transformation = 'identity()',
traindays = 5,
testdays = 2,
xreg = 'fourier(., h=h, K=2)')
# no SAR, no dummies, 3 AR terms
# rmse=460, mae=237
report.full(model = 'Arima(order=c(3, 0, 0), method="CSS", xreg=fourier(., K=2))',
series = '1hrs pv1',
transformation = 'identity()',
traindays = 5,
testdays = 2,
xreg = 'fourier(., h=h, K=2)')
# dummies on every weekday ----
dailyD.fcast <- quote(
{cbind(
dummies=getDailyDummies(h, frequency(.), start(.)[[1]]),
fourier(., h=h, K=2)
)}
)
dailyD.fit <- quote(
{cbind(
dummies=getDailyDummies(length(.), frequency(.), start(.)[[1]]),
fourier(., K=2)
)}
)
# 5:2 rmse=?, mae=?
report.full(model = paste0('Arima(order=c(1, 0, 0), seasonal=c(1, 0, 0), method="CSS", xreg=', paste0(deparse(dailyD.fit), collapse='') ,')'),
series = '1hrs pv1',
transformation = 'identity()',
traindays = 5,
testdays = 2,
xreg = paste0(deparse(dailyD.fcast), collapse=''))
# dummies on the ?-?th+?-?th obs (the "outliers") ----
best.fcast.dummy.1hrsPv1 <- NULL
best.startDummy <- 0
best.lenDummy <- 0
for(startDummy in 8:11)
{
for(lenDummy in 5:9)
{
print(paste("Trying startDummy =", startDummy, ", length =", lenDummy))
obsDummies.fcast <- substitute(
{cbind(
dummies=getNthObsDummies(startDummy, lenDummy, h, frequency(.)),
fourier(., h=h, K=2)
)},
list(startDummy=startDummy, lenDummy=lenDummy)
)
obsDummies.fit <- substitute(
{cbind(
dummies=getNthObsDummies(startDummy, lenDummy, length(.), frequency(.)),
fourier(., K=2)
)},
list(startDummy=startDummy, lenDummy=lenDummy)
)
current <- fullforecast(model = paste0('Arima(order=c(1, 0, 0), seasonal=c(1, 0, 0), method="CSS", xreg=', paste0(deparse(obsDummies.fit), collapse='') ,')'),
dataset = datasets[['1hrs pv1']]$series,
transformation = 'identity()',
traindays = 5,
testdays = 2,
xreg = paste0(deparse(obsDummies.fcast), collapse=''))
if(is.null(best.fcast.dummy.1hrsPv1) || current$accuracy[[2]] < best.fcast.dummy.1hrsPv1$accuracy[[2]])
{
best.fcast.dummy.1hrsPv1 <- current
best.startDummy <- startDummy
best.lenDummy <- lenDummy
}
}
}
bestObsDummies.fcast <- substitute(
{cbind(
dummies=getNthObsDummies(best.startDummy, best.lenDummy, h, frequency(.)),
fourier(., h=h, K=2)
)},
list(best.startDummy = best.startDummy, best.lenDummy = best.lenDummy)
)
bestObsDummies.fit <- substitute(
{cbind(
dummies=getNthObsDummies(best.startDummy, best.lenDummy, length(.), frequency(.)),
fourier(., K=2)
)},
list(best.startDummy = best.startDummy, best.lenDummy = best.lenDummy)
)
# 5:2, dummies: 11:6, rmse=461, mae=230
report.full(model = paste0('Arima(order=c(1, 0, 0), seasonal=c(1, 0, 0), method="CSS", xreg=', paste0(deparse(bestObsDummies.fit), collapse='') ,')'),
series = '1hrs pv1',
transformation = 'identity()',
traindays = 5,
testdays = 2,
xreg = paste0(deparse(bestObsDummies.fcast), collapse=''))