Description Usage Arguments Details Value Note Examples
Creates an ARIMA model that is then fitted to the data as a univariate time series. If further variables are specified in the model, it also includess exogenous variables. The order (p, d, q) is tuned by choosing the one with best fit.
1 | auto_arima_model(p = 5, d = 2, q = 5, intercept = TRUE, ...)
|
p |
Maximum order of auto-regressive (AR) terms that is tested to find the best fit (default: 5). |
d |
Maximum degree of differencing that is tested to find the best fit (default: 2). |
q |
Maximum order of moving-average (MA) term that is tested to find the best fit (default: 5). |
intercept |
Boolean value whether to include an intercept term (default:
|
... |
Further arguments used when fitting ARIMA model. |
Variable importance metrics return the absolute value of the coefficients for the exogenous variables (if any).
Model definition that can then be insered into train
.
If one desires an ARIMA model of fixed, pre-defined order, then one needs to
switch to auto_arima_model
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 | library(caret)
# without exogenous variables
library(forecast)
data(WWWusage) # from package "forecast"
df <- data.frame(y = as.numeric(WWWusage))
lm <- train(y ~ 1, data = df, method = "lm", trControl = trainDirectFit())
summary(lm)
RMSE(predict(lm, df), df)
arima <- train(y ~ 1, data = df, method = auto_arima_model(), trControl = trainDirectFit())
summary(arima)
RMSE(predict(arima, df), df)
# with exogenous variables
library(vars)
data(Canada)
arima <- train(x = Canada[, -2], y = Canada[, 2],
method = auto_arima_model(), trControl = trainDirectFit())
summary(arima)
arimaorder(arima$finalModel) # order of best model
predict(arima, Canada[, -2]) # in-sample predictions
RMSE(predict(arima, Canada[, -2]), Canada[, 2]) # in-sample RMSE
absCoef <- varImp(arima, scale = FALSE) # variable importance (= absolute value of coefficient)
absCoef
plot(absCoef)
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