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forecasting.R
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forecasting.R
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simRpart <- function(Y, K, freq = 48, h = 48){
data_ts <- msts(Y, seasonal.periods = c(freq, freq*7))
fuur <- fourier(data_ts, K = c(K, K*2))
fuur_test <- as.data.frame(fourier(data_ts, K = c(K, K*2), h = h))
data_ts <- ts(Y, freq = freq*7)
decom_1 <- stl(data_ts, s.window = "periodic", robust = T)
new_load <- rowSums(decom_1$time.series[, c(1,3)])
trend_part <- ts(decom_1$time.series[,2])
trend_fit <- auto.arima(trend_part)
trend_for <- as.vector(forecast(trend_fit, h)$mean)
matrix_train <- data.table(Load = new_load,
fuur)
tree_1 <- rpart(Load ~ ., data = matrix_train,
control = rpart.control(minsplit = 2,
maxdepth = 30,
cp = 0.000001))
# new data and prediction
pred_tree <- predict(tree_1, fuur_test) + trend_for
return(as.vector(pred_tree))
}
simCtreeLag <- function(Y, freq = 48, h = 48){
N <- length(Y)
window <- (N / freq) - 1
K <- 2
data_ts <- msts(Y, seasonal.periods = c(freq, freq*7))
fuur <- fourier(data_ts, K = c(K, K*2))
fuur_test <- as.data.frame(fourier(data_ts, K = c(K, K*2), h = h))
data_ts <- ts(Y, freq = freq*7)
decom_1 <- stl(data_ts, s.window = "periodic", robust = T)
new_load <- rowSums(decom_1$time.series[, c(1,3)])
trend_part <- ts(decom_1$time.series[,2])
trend_fit <- auto.arima(trend_part)
trend_for <- as.vector(forecast(trend_fit, h)$mean)
train_s <- decom_1$time.series[1:(freq*window), 1]
matrix_trigonom <- data.table(Load = tail(new_load, window*freq),
fuur[(freq+1):N,],
Seas.Ts = train_s)
test_s <- decom_1$time.series[((freq*window)+1):(freq*(window+1)), 1]
matrix_test <- data.table(fuur_test,
Seas.Ts = test_s)
tree_2 <- party::ctree(Load ~ ., data = matrix_trigonom,
controls = party::ctree_control(teststat = c("quad"),
testtype = c("Teststatistic"),
mincriterion = 0.95,
minsplit = 1,
minbucket = 1,
mtry = 0, maxdepth = 0))
pred_tree <- predict(tree_2, matrix_test) + trend_for
return(as.vector(pred_tree))
}
simRFLag <- function(Y, freq = 48, h = 48){
N <- length(Y)
window <- (N / freq) - 1
K <- 2
data_ts <- msts(Y, seasonal.periods = c(freq, freq*7))
fuur <- fourier(data_ts, K = c(K, K*2))
fuur_test <- fourier(data_ts, K = c(K, K*2), h = h)
colnames(fuur) <- NULL
colnames(fuur_test) <- NULL
data_ts <- ts(Y, freq = freq*7)
decom_1 <- stl(data_ts, s.window = "periodic", robust = T)
new_load <- rowSums(decom_1$time.series[, c(1,3)])
trend_part <- ts(decom_1$time.series[,2])
trend_fit <- auto.arima(trend_part)
trend_for <- as.vector(forecast(trend_fit, h)$mean)
train_s <- decom_1$time.series[1:(freq*window), 1]
matrix_trigonom <- data.table(Load = tail(new_load, window*freq),
fuur[(freq+1):N,],
Seas.Ts = train_s)
test_s <- decom_1$time.series[((freq*window)+1):(freq*(window+1)), 1]
matrix_test <- data.table(fuur_test,
Seas.Ts = test_s)
rf_model <- randomForest(Load ~ ., data = matrix_trigonom,
ntree = 1100, mtry = 3, nodesize = 3, importance = TRUE)
pred_rf <- predict(rf_model, matrix_test) + trend_for
return(as.vector(pred_rf))
}
simCtreeFur <- function(Y, K, freq = 48, h = 48){
data_ts <- msts(Y, seasonal.periods = c(freq, freq*7))
fuur <- fourier(data_ts, K = c(K, K*2))
fuur_test <- as.data.table(fourier(data_ts, K = c(K, K*2), h = h))
data_ts <- ts(Y, freq = freq*7)
decom_1 <- stl(data_ts, s.window = "periodic", robust = T)
new_load <- rowSums(decom_1$time.series[, c(1,3)])
trend_part <- ts(decom_1$time.series[,2])
trend_fit <- auto.arima(trend_part)
trend_for <- as.vector(forecast(trend_fit, h)$mean)
matrix_train <- data.table(Load = new_load,
fuur)
tree_2 <- party::ctree(Load ~ ., data = matrix_train,
controls = party::ctree_control(teststat = c("quad"),
testtype = c("Teststatistic"),
mincriterion = 0.95,
minsplit = 1,
minbucket = 1,
mtry = 0, maxdepth = 0))
pred_tree <- predict(tree_2, fuur_test) + trend_for
return(as.vector(pred_tree))
}
simRFFur <- function(Y, K, freq = 48, h = 48){
data_ts <- msts(Y, seasonal.periods = c(freq, freq*7))
fuur <- fourier(data_ts, K = c(K, K*2))
fuur_test <- fourier(data_ts, K = c(K, K*2), h = h)
colnames(fuur) <- NULL
colnames(fuur_test) <- NULL
data_ts <- ts(Y, freq = freq*7)
decom_1 <- stl(data_ts, s.window = "periodic", robust = T)
new_load <- rowSums(decom_1$time.series[, c(1,3)])
trend_part <- ts(decom_1$time.series[,2])
trend_fit <- auto.arima(trend_part)
trend_for <- as.vector(forecast(trend_fit, h)$mean)
matrix_train <- data.table(Load = new_load,
fuur)
rf_model <- randomForest(Load ~ ., data = matrix_train,
ntree = 1100, mtry = 4,
nodesize = 3, importance = TRUE)
pred_tree <- predict(rf_model, data.table(fuur_test)) + trend_for
return(as.vector(pred_tree))
}
simEsExtra <- function(Y, freq = 48, h = 48){
data_ts <- ts(Y, freq = freq*7)
pred <- es(data_ts, model = c("AAA", "ANA", "AAdA"), ic = c("AIC"), h = h, cfType = "MAE", intervals = "none", silent = "all")$forecast
return(as.vector(pred))
}
# All methods in one FUN ----
predSimAll <- function(Y, freq = 48, h = 48){
pred_rpart_2 <- simRpart(Y, 2, freq = freq, h = h)
pred_ctree_lag <- simCtreeLag(Y, freq = freq, h = h)
pred_ctree_fur <- simCtreeFur(Y, 5, freq = freq, h = h)
pred_rf_lag <- simRFLag(Y, freq = freq, h = h)
pred_rf_fur <- simRFFur(Y, 3, freq = freq, h = h)
pred_ese <- simEsExtra(Y, freq = freq, h = h)
return(list(RPART1 = pred_rpart_2,
CTREE_LAG = pred_ctree_lag,
CTREE_FUR = pred_ctree_fur,
RF_LAG = pred_rf_lag,
RF_FUR = pred_rf_fur,
ESE = pred_ese))
}