Forecast the actual values of a transformed time series in ARIMAX model in R
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I am fitting an ARIMAx model to my time series data. The "data" includes "Rate" as the outcome and x1, x2, and x3 as covariates and I have transformed the outcome using Box-Cox transformation. My data is split into a train and a test set and I want to forecast the test set with the actual values and not the transformed values. I have done the following:
data.train <- ts(data[1:24, ] , frequency = 4, start = c(2011, 4)
data.test <- ts(data[25:28, ], frequency = 4, start = c(2017, 4))
covariates <- c("x1", "x2", "x3")
xreg.train <- data.train[, covariates]
xreg.test <- data.test[, covariates]
outcome <- data.train[, "Rate"]
lambda <- BoxCox.lambda(outcome)
outcome.trans <- BoxCox(outcome, lambda)
fit <- auto.arima(outcome.trans, xreg = xreg.train, trace = TRUE, stepwise = FALSE, seasonal = TRUE)
Now I want to forecast the test set with the actual values and not the transformed values:
forecast.test <- predict(fit, newxreg = xreg.test, lambda = lambda)
Now the PROBLEM is that this predict function produces a forecast of the transformed values and not the actual values. How can I get the forecast of the actual values without doing the transformation myself.
r time-series transformation arima
edited Nov 23 at 2:14
jsb
1,47911426
asked Nov 20 at 17:51
sahboor
387
add a comment |
up vote
0
down vote
favorite
I am fitting an ARIMAx model to my time series data. The "data" includes "Rate" as the outcome and x1, x2, and x3 as covariates and I have transformed the outcome using Box-Cox transformation. My data is split into a train and a test set and I want to forecast the test set with the actual values and not the transformed values. I have done the following:
data.train <- ts(data[1:24, ] , frequency = 4, start = c(2011, 4)
data.test <- ts(data[25:28, ], frequency = 4, start = c(2017, 4))
covariates <- c("x1", "x2", "x3")
xreg.train <- data.train[, covariates]
xreg.test <- data.test[, covariates]
outcome <- data.train[, "Rate"]
lambda <- BoxCox.lambda(outcome)
outcome.trans <- BoxCox(outcome, lambda)
fit <- auto.arima(outcome.trans, xreg = xreg.train, trace = TRUE, stepwise = FALSE, seasonal = TRUE)
Now I want to forecast the test set with the actual values and not the transformed values:
forecast.test <- predict(fit, newxreg = xreg.test, lambda = lambda)
Now the PROBLEM is that this predict function produces a forecast of the transformed values and not the actual values. How can I get the forecast of the actual values without doing the transformation myself.
r time-series transformation arima
edited Nov 23 at 2:14
jsb
1,47911426
asked Nov 20 at 17:51
sahboor
387
add a comment |
up vote
0
down vote
favorite
up vote
0
down vote
favorite
I am fitting an ARIMAx model to my time series data. The "data" includes "Rate" as the outcome and x1, x2, and x3 as covariates and I have transformed the outcome using Box-Cox transformation. My data is split into a train and a test set and I want to forecast the test set with the actual values and not the transformed values. I have done the following:
data.train <- ts(data[1:24, ] , frequency = 4, start = c(2011, 4)
data.test <- ts(data[25:28, ], frequency = 4, start = c(2017, 4))
covariates <- c("x1", "x2", "x3")
xreg.train <- data.train[, covariates]
xreg.test <- data.test[, covariates]
outcome <- data.train[, "Rate"]
lambda <- BoxCox.lambda(outcome)
outcome.trans <- BoxCox(outcome, lambda)
fit <- auto.arima(outcome.trans, xreg = xreg.train, trace = TRUE, stepwise = FALSE, seasonal = TRUE)
Now I want to forecast the test set with the actual values and not the transformed values:
forecast.test <- predict(fit, newxreg = xreg.test, lambda = lambda)
Now the PROBLEM is that this predict function produces a forecast of the transformed values and not the actual values. How can I get the forecast of the actual values without doing the transformation myself.
r time-series transformation arima
edited Nov 23 at 2:14
jsb
1,47911426
asked Nov 20 at 17:51
sahboor
387
I am fitting an ARIMAx model to my time series data. The "data" includes "Rate" as the outcome and x1, x2, and x3 as covariates and I have transformed the outcome using Box-Cox transformation. My data is split into a train and a test set and I want to forecast the test set with the actual values and not the transformed values. I have done the following:
data.train <- ts(data[1:24, ] , frequency = 4, start = c(2011, 4)
data.test <- ts(data[25:28, ], frequency = 4, start = c(2017, 4))
covariates <- c("x1", "x2", "x3")
xreg.train <- data.train[, covariates]
xreg.test <- data.test[, covariates]
outcome <- data.train[, "Rate"]
lambda <- BoxCox.lambda(outcome)
outcome.trans <- BoxCox(outcome, lambda)
fit <- auto.arima(outcome.trans, xreg = xreg.train, trace = TRUE, stepwise = FALSE, seasonal = TRUE)
Now I want to forecast the test set with the actual values and not the transformed values:
forecast.test <- predict(fit, newxreg = xreg.test, lambda = lambda)
Now the PROBLEM is that this predict function produces a forecast of the transformed values and not the actual values. How can I get the forecast of the actual values without doing the transformation myself.
r time-series transformation arima
r time-series transformation arima
edited Nov 23 at 2:14
jsb
1,47911426
asked Nov 20 at 17:51
sahboor
387
edited Nov 23 at 2:14
jsb
1,47911426
asked Nov 20 at 17:51
sahboor
387
edited Nov 23 at 2:14
jsb
1,47911426
edited Nov 23 at 2:14
jsb
1,47911426
edited Nov 23 at 2:14
jsb
1,47911426
1,47911426
asked Nov 20 at 17:51
sahboor
387
asked Nov 20 at 17:51
sahboor
387
asked Nov 20 at 17:51
sahboor
387
387
add a comment |
add a comment |
1 Answer
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The forecast package does all this for you. But you need to use the forecast() function, not the predict() function.
data.train <- ts(data[1:24,] ,frequency=4, start=c(2011,4)
data.test <- ts(data[25:28,], frequency=4, start=c(2017,4))
covariates <- c("x1","x2","x3")
xreg.train <- data.train[, covariates]
xreg.test <- data.test[, covariates]
outcome <- data.train[,"Rate"]
lambda <- BoxCox.lambda(outcome)
fit<- auto.arima(outcome, xreg=xreg.train, lambda=lambda,
trace=TRUE, stepwise=FALSE, seasonal=TRUE, lambda=lambda)
forecast.test <- forecast(fit, xreg=xreg.test, lambda=lambda)
answered Nov 21 at 11:07
Rob Hyndman
20.2k64666
Rob when i use your command i get this error: Error in forecast(fit, newxreg = xreg.test, : unused arguments (newxreg = xreg.test, lambda = lambda) seems like it doesnt recognize the newxreg command within the forecast function
– sahboor
Nov 21 at 13:32
1
Sorry. Now fixed. you need to use xreg, not newxreg, with forecast()
– Rob Hyndman
Nov 21 at 20:00
thank you so much Rob, fixed
– sahboor
Nov 21 at 21:30
add a comment |
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1 Answer
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oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
up vote
2
down vote
The forecast package does all this for you. But you need to use the forecast() function, not the predict() function.
data.train <- ts(data[1:24,] ,frequency=4, start=c(2011,4)
data.test <- ts(data[25:28,], frequency=4, start=c(2017,4))
covariates <- c("x1","x2","x3")
xreg.train <- data.train[, covariates]
xreg.test <- data.test[, covariates]
outcome <- data.train[,"Rate"]
lambda <- BoxCox.lambda(outcome)
fit<- auto.arima(outcome, xreg=xreg.train, lambda=lambda,
trace=TRUE, stepwise=FALSE, seasonal=TRUE, lambda=lambda)
forecast.test <- forecast(fit, xreg=xreg.test, lambda=lambda)
answered Nov 21 at 11:07
Rob Hyndman
20.2k64666
Rob when i use your command i get this error: Error in forecast(fit, newxreg = xreg.test, : unused arguments (newxreg = xreg.test, lambda = lambda) seems like it doesnt recognize the newxreg command within the forecast function
– sahboor
Nov 21 at 13:32
1
Sorry. Now fixed. you need to use xreg, not newxreg, with forecast()
– Rob Hyndman
Nov 21 at 20:00
thank you so much Rob, fixed
– sahboor
Nov 21 at 21:30
add a comment |
up vote
2
down vote
The forecast package does all this for you. But you need to use the forecast() function, not the predict() function.
data.train <- ts(data[1:24,] ,frequency=4, start=c(2011,4)
data.test <- ts(data[25:28,], frequency=4, start=c(2017,4))
covariates <- c("x1","x2","x3")
xreg.train <- data.train[, covariates]
xreg.test <- data.test[, covariates]
outcome <- data.train[,"Rate"]
lambda <- BoxCox.lambda(outcome)
fit<- auto.arima(outcome, xreg=xreg.train, lambda=lambda,
trace=TRUE, stepwise=FALSE, seasonal=TRUE, lambda=lambda)
forecast.test <- forecast(fit, xreg=xreg.test, lambda=lambda)
answered Nov 21 at 11:07
Rob Hyndman
20.2k64666
Rob when i use your command i get this error: Error in forecast(fit, newxreg = xreg.test, : unused arguments (newxreg = xreg.test, lambda = lambda) seems like it doesnt recognize the newxreg command within the forecast function
– sahboor
Nov 21 at 13:32
1
Sorry. Now fixed. you need to use xreg, not newxreg, with forecast()
– Rob Hyndman
Nov 21 at 20:00
thank you so much Rob, fixed
– sahboor
Nov 21 at 21:30
add a comment |
up vote
2
down vote
up vote
2
down vote
The forecast package does all this for you. But you need to use the forecast() function, not the predict() function.
data.train <- ts(data[1:24,] ,frequency=4, start=c(2011,4)
data.test <- ts(data[25:28,], frequency=4, start=c(2017,4))
covariates <- c("x1","x2","x3")
xreg.train <- data.train[, covariates]
xreg.test <- data.test[, covariates]
outcome <- data.train[,"Rate"]
lambda <- BoxCox.lambda(outcome)
fit<- auto.arima(outcome, xreg=xreg.train, lambda=lambda,
trace=TRUE, stepwise=FALSE, seasonal=TRUE, lambda=lambda)
forecast.test <- forecast(fit, xreg=xreg.test, lambda=lambda)
answered Nov 21 at 11:07
Rob Hyndman
20.2k64666
The forecast package does all this for you. But you need to use the forecast() function, not the predict() function.
data.train <- ts(data[1:24,] ,frequency=4, start=c(2011,4)
data.test <- ts(data[25:28,], frequency=4, start=c(2017,4))
covariates <- c("x1","x2","x3")
xreg.train <- data.train[, covariates]
xreg.test <- data.test[, covariates]
outcome <- data.train[,"Rate"]
lambda <- BoxCox.lambda(outcome)
fit<- auto.arima(outcome, xreg=xreg.train, lambda=lambda,
trace=TRUE, stepwise=FALSE, seasonal=TRUE, lambda=lambda)
forecast.test <- forecast(fit, xreg=xreg.test, lambda=lambda)
answered Nov 21 at 11:07
Rob Hyndman
20.2k64666
edited Nov 21 at 19:59
answered Nov 21 at 11:07
Rob Hyndman
20.2k64666
answered Nov 21 at 11:07
Rob Hyndman
20.2k64666
answered Nov 21 at 11:07
Rob Hyndman
20.2k64666
20.2k64666
Rob when i use your command i get this error: Error in forecast(fit, newxreg = xreg.test, : unused arguments (newxreg = xreg.test, lambda = lambda) seems like it doesnt recognize the newxreg command within the forecast function
– sahboor
Nov 21 at 13:32
1
Sorry. Now fixed. you need to use xreg, not newxreg, with forecast()
– Rob Hyndman
Nov 21 at 20:00
thank you so much Rob, fixed
– sahboor
Nov 21 at 21:30
add a comment |
Rob when i use your command i get this error: Error in forecast(fit, newxreg = xreg.test, : unused arguments (newxreg = xreg.test, lambda = lambda) seems like it doesnt recognize the newxreg command within the forecast function
– sahboor
Nov 21 at 13:32
1
Sorry. Now fixed. you need to use xreg, not newxreg, with forecast()
– Rob Hyndman
Nov 21 at 20:00
thank you so much Rob, fixed
– sahboor
Nov 21 at 21:30
Rob when i use your command i get this error: Error in forecast(fit, newxreg = xreg.test, : unused arguments (newxreg = xreg.test, lambda = lambda) seems like it doesnt recognize the newxreg command within the forecast function
– sahboor
Nov 21 at 13:32
Rob when i use your command i get this error: Error in forecast(fit, newxreg = xreg.test, : unused arguments (newxreg = xreg.test, lambda = lambda) seems like it doesnt recognize the newxreg command within the forecast function
– sahboor
Nov 21 at 13:32
1
1
Sorry. Now fixed. you need to use xreg, not newxreg, with forecast()
– Rob Hyndman
Nov 21 at 20:00
Sorry. Now fixed. you need to use xreg, not newxreg, with forecast()
– Rob Hyndman
Nov 21 at 20:00
thank you so much Rob, fixed
– sahboor
Nov 21 at 21:30
thank you so much Rob, fixed
– sahboor
Nov 21 at 21:30
add a comment |
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