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2hrs_ph2_report_generator.R
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2hrs_ph2_report_generator.R
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source('funcs.R')
# benchmark models ----
report.full(model = 'snaive()',
series = '2hrs ph2',
transformation = 'identity()',
traindays = 7,
testdays = 1)
report.full(model = 'meanf()',
series = '2hrs ph2',
transformation = 'identity()',
traindays = 7,
testdays = 1)
report.full(model = 'naive()',
series = '2hrs ph2',
transformation = 'identity()',
traindays = 7,
testdays = 1)
# try to find the best ARIMA model ----
# sinusoidal ACF and 1 lag in PACF
report(model = 'Arima(order=c(1, 0, 0))',
series = '2hrs ph2',
transformation = 'identity()',
diffs = 'identity()',
sdiffs = 'identity()',
startday = -10,
traindays = 7,
testdays = 3)
# significant values in lags of order 12
report(model = 'Arima(order=c(1, 0, 0), seasonal=c(1, 0, 0))',
series = '2hrs ph2',
transformation = 'identity()',
diffs = 'identity()',
sdiffs = 'identity()',
startday = -10,
traindays = 7,
testdays = 3)
# seems fine, but the forecast is overall a bit too high
report(model = 'Arima(order=c(1, 0, 0), seasonal=c(1, 0, 0), include.mean=FALSE)',
series = '2hrs ph2',
transformation = 'identity()',
diffs = 'identity()',
sdiffs = 'identity()',
startday = -10,
traindays = 7,
testdays = 3)
# try with MA & seasonal MA terms since the data could fit the "pattern" - worse than AR
report(model = 'Arima(order=c(0, 0, 1), seasonal=c(0, 0, 1))',
series = '2hrs ph2',
transformation = 'identity()',
diffs = 'identity()',
sdiffs = 'identity()',
startday = -10,
traindays = 7,
testdays = 3)
# regression with ARMA errors on fourier terms
report(model = 'Arima(order=c(1, 0, 0), seasonal=c(1, 0, 0), xreg=fourier(., K=2))',
series = '2hrs ph2',
transformation = 'identity()',
diffs = 'identity()',
sdiffs = 'identity()',
startday = -8,
traindays = 7,
testdays = 1,
xreg='fourier(., K=2, h=h)')
# Find the best train:test days ratio for ARIMA(1,0,0)(1,0,0) ----
best.fcast.2hrsPh2 <- NULL
best.traindays <- 0
best.testdays <- 0
for(traindays in 3:7)
{
for(testdays in 1:3)
{
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[['2hrs ph2']]$series,
transformation = 'identity()',
traindays = traindays,
testdays = testdays,
xreg=NULL)
if(is.null(best.fcast.2hrsPh2) || current$accuracy[[2]] < best.fcast.2hrsPh2$accuracy[[2]])
{
best.fcast.2hrsPh2 <- current
best.traindays <- traindays
best.testdays <- testdays
}
}
}
report.full(model = 'Arima(order=c(1, 0, 0), seasonal=c(1, 0, 0), method="ML")',
series = '2hrs ph2',
transformation = 'identity()',
traindays = best.traindays, # 7
testdays = best.testdays) # 3
# Skip over the step where I hardcode a fourier value ----
# Find best K for the above model ARIMA(1,0,0)(1,0,0) ----
best.fcast.k.2hrsPh2 <- NULL
best.k <- 0
#K must be not be greater than period/2
for(k in 1:(frequency(datasets[['2hrs ph2']]$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[['2hrs ph2']]$series,
transformation = 'identity()',
traindays = best.traindays, # 7
testdays = best.testdays, # 3
xreg=xreg)
if(is.null(best.fcast.k.2hrsPh2) || current$accuracy[[2]] < best.fcast.k.2hrsPh2$accuracy[[2]])
{
best.fcast.k.2hrsPh2 <- 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 = '2hrs ph2',
transformation = 'identity()',
traindays = best.traindays, # 7
testdays = best.testdays, # 3
xreg = paste('fourier(., h=h, K=', best.k, ')')) #1
# Best model: 7:3, ARIMA(1, 0, 0)(1, 0, 0), K=1, RMSE=373.8851 MAE=184.5098 ----
# dummies 4:4 RMSE=370 MAE=178
# no SAR term, dummies 4:4 RMSE=366 MAE=178
report.full(model = 'Arima(order=c(1, 0, 0), seasonal=c(1, 0, 0), method="CSS", xreg=fourier(., K=1))',
series = '2hrs ph2',
transformation = 'identity()',
traindays = 7,
testdays = 3,
xreg = 'fourier(., h=h, K=1)')
bestObsDummies.fcast <- substitute(
{cbind(
dummies=getNthObsDummies(4, 4, h, frequency(.)),
fourier(., h=h, K=1)
)}
)
bestObsDummies.fit <- substitute(
{cbind(
dummies=getNthObsDummies(4, 4, length(.), frequency(.)),
fourier(., K=1)
)}
)
# 7:3, dummies 4:4, rmse=370.118, mae=178.8
report.full(model = paste0('Arima(order=c(1, 0, 0), seasonal=c(1, 0, 0), method="ML", xreg=', paste0(deparse(bestObsDummies.fit), collapse='') ,')'),
series = '2hrs ph2',
transformation = 'identity()',
traindays = 7,
testdays = 3,
xreg = paste0(deparse(bestObsDummies.fcast), collapse=''))
report.full(model = paste0('Arima(order=c(1, 0, 0), method="ML", xreg=', paste0(deparse(bestObsDummies.fit), collapse='') ,')'),
series = '2hrs ph2',
transformation = 'identity()',
traindays = 7,
testdays = 3,
xreg = paste0(deparse(bestObsDummies.fcast), collapse=''))
# rmse=374 mae=185 - worse
report.full(model = 'Arima(order=c(1, 0, 0), method="CSS", xreg=fourier(., K=1))',
series = '2hrs ph2',
transformation = 'identity()',
traindays = 7,
testdays = 3,
xreg = 'fourier(., h=h, K=1)')
# dummies on 6th day - 6th day has an "outlier" ----
sixthDD.fcast <- quote(
{cbind(
dummies=get6thDayDummies(h, frequency(.), start(.)[[1]]),
fourier(., h=h, K=1)
)}
)
sixthDD.fit <- quote(
{cbind(
dummies=get6thDayDummies(length(.), frequency(.), start(.)[[1]]),
fourier(., K=1)
)}
)
#7:3 rmse=408 mae=198
report.full(model = paste0('Arima(order=c(1, 0, 0), seasonal=c(1, 0, 0), method="CSS", xreg=', paste0(deparse(sixthDD.fit), collapse='') ,')'),
series = '2hrs ph2',
transformation = 'identity()',
traindays = 7,
testdays = 3,
xreg = paste0(deparse(sixthDD.fcast), collapse=''))
# dummies on every weekday ----
dailyD.fcast <- quote(
{cbind(
dummies=getDailyDummies(h, frequency(.), start(.)[[1]]),
fourier(., h=h, K=1)
)}
)
dailyD.fit <- quote(
{cbind(
dummies=getDailyDummies(length(.), frequency(.), start(.)[[1]]),
fourier(., K=1)
)}
)
# 7:3 rmse=382, mae=196
report.full(model = paste0('Arima(order=c(1, 0, 0), seasonal=c(1, 0, 0), method="CSS", xreg=', paste0(deparse(dailyD.fit), collapse='') ,')'),
series = '2hrs ph2',
transformation = 'identity()',
traindays = 7,
testdays = 3,
xreg = paste0(deparse(dailyD.fcast), collapse=''))
# dummies on the 5th obs (the "outlier") ----
fifthOD.fcast <- quote(
{cbind(
dummies=getNthObsDummies(5, 1, h, frequency(.)),
fourier(., h=h, K=1)
)}
)
fifthOD.fit <- quote(
{cbind(
dummies=getNthObsDummies(5, 1, length(.), frequency(.)),
fourier(., K=1)
)}
)
# 7:3 rmse=376, mae=183
report.full(model = paste0('Arima(order=c(1, 0, 0), seasonal=c(1, 0, 0), method="CSS", xreg=', paste0(deparse(fifthOD.fit), collapse='') ,')'),
series = '2hrs ph2',
transformation = 'identity()',
traindays = 7,
testdays = 3,
xreg = paste0(deparse(fifthOD.fcast), collapse=''))
# dummies on the 4-8th+1-5th obs (the "outliers") ----
best.fcast.dummy.2hrsPh2 <- NULL
best.startDummy <- 0
best.lenDummy <- 0
for(startDummy in 4:8)
{
for(lenDummy in 1:5)
{
print(paste("Trying startDummy =", startDummy, ", length =", lenDummy))
obsDummies.fcast <- substitute(
{cbind(
dummies=getNthObsDummies(startDummy, lenDummy, h, frequency(.)),
fourier(., h=h, K=1)
)},
list(startDummy=startDummy, lenDummy=lenDummy)
)
obsDummies.fit <- substitute(
{cbind(
dummies=getNthObsDummies(startDummy, lenDummy, length(.), frequency(.)),
fourier(., K=1)
)},
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[['2hrs ph2']]$series,
transformation = 'identity()',
traindays = 7,
testdays = 3,
xreg = paste0(deparse(obsDummies.fcast), collapse=''))
if(is.null(best.fcast.dummy.2hrsPh2) || current$accuracy[[2]] < best.fcast.dummy.2hrsPh2$accuracy[[2]])
{
best.fcast.dummy.2hrsPh2 <- current
best.startDummy <- startDummy
best.lenDummy <- lenDummy
}
}
}
bestObsDummies.fcast <- substitute(
{cbind(
dummies=getNthObsDummies(best.startDummy, best.lenDummy, h, frequency(.)),
fourier(., h=h, K=1)
)},
list(best.startDummy = best.startDummy, best.lenDummy = best.lenDummy)
)
bestObsDummies.fit <- substitute(
{cbind(
dummies=getNthObsDummies(best.startDummy, best.lenDummy, length(.), frequency(.)),
fourier(., K=1)
)},
list(best.startDummy = best.startDummy, best.lenDummy = best.lenDummy)
)
# 7:3, dummies 4:4, rmse=370.118, mae=178.8
report.full(model = paste0('Arima(order=c(1, 0, 0), seasonal=c(1, 0, 0), method="ML", xreg=', paste0(deparse(bestObsDummies.fit), collapse='') ,')'),
series = '2hrs ph2',
transformation = 'identity()',
traindays = 7,
testdays = 3,
xreg = paste0(deparse(bestObsDummies.fcast), collapse=''))