tts.autoML: Train time series by automatic machine learning of 'h2o'...

In iForecast: Machine Learning Time Series Forecasting

Train time series by automatic machine learning of h2o provided by H2o.ai

Description

It applies the h2o.autoML of H2O.ai to time series data.

Usage

tts.autoML(y,x=NULL,train.end,arOrder=2,xregOrder=0,type,max_models = 20,
                      sort_metric="AUTO",stopping_metric = "AUTO",initial=TRUE)

Arguments

y	The time series object of the target variable, for example, timeSeries,xts, or zoo. Numerically,y must be real numbers for regression or integers for classification. Date format must be "
x	The time series matrix of input variables, timestamp is the same as y, maybe null.
train.end	The end date of training data, must be specificed. The default dates of train.start and test.end are the start and the end of input data; and the test.start is the 1-period next of train.end.
arOrder	The autoregressive order of the target variable, which may be sequentially specifed like arOrder=1:5; or discontinuous lags like arOrder=c(1,3,5); zero is not allowed.
xregOrder	The distributed lag structure of the input variables, which may be sequentially specifed like xregOrder=1:5; or discontinuous lags like xregOrder=c(0,3,5); zero is allowed since contemporaneous correlation is allowed.
type	The time dummies variables. We have four selection: 'none'=no other variables, 'trend'=inclusion of time dummy, 'season'=inclusion of seasonal dummies, 'both'=inclusion of both trend and season. No default.
max_models	Number of AutoML base models, default to 20.
sort_metric	Specifies the metric used to sort the Leaderboard by at the end of an AutoML run. Defaults to "AUTO", where 'AUC' (area under the ROC curve) for binary classification, 'mean_per_class_error' for multinomial classification, and 'deviance' for regression. Available options include:'MSE','RMSE','MAE','RMSLE','AUCPR' (area under the Precision-Recall curve)
stopping_metric	Specify the metric to use for early stopping. Defaults to "AUTO",where 'logloss' for classification and 'deviance' for regression. Besides, options are: 'MSE','RMSE','MAE','RMSLE','AUC','AUCPR','lift_top_group'
initial	Whether to initialize h2o.init(). Default to "TRUE" and, to avoid multiple initiations, users had better change it to FALSE while training via rolling windows. See example below.

Details

This function calls the h2o.automl function from package h2o to execute automatic machine learning estimation.

Value

output	Output object generated by h2o.automl function of h2o.
modelsUsed	AutoML Leaderboard object, which is a table returns the argument of 'max_models'.
arOrder	The autoregressive order of the target variable used.
dataused	The data used by arOrder, xregOrder
data	The complete data structure
TD	Time dummies used, inherited from 'type' in tts.autoML
train.end	The same as the argument in tts.caret

Author(s)

Ho Tsung-wu <[e-mail]>, College of Management, National Taiwan Normal University.

Examples

# Computation takes time, example below is commented.
data("macrodata")
dep<-macrodata[,"unrate",drop=FALSE]
ind<-macrodata[,-1,drop=FALSE]

# Choosing the dates of training and testing data
train.end<-"2008-12-01"

#autoML of H2O.ai must execute the commands below
#h2o::h2o.init()        # Initialize h2o
#invisible(h2o::h2o.no_progress()) # Turn off progress bars

# autoML <- tts.autoML(y=dep, x=ind, train.end,arOrder=c(2,4),
# xregOrder=c(0,1,3),type="both",initial=FALSE)
# print(autoML$modelsUsed,n=22) #View the AutoML Leaderboard

#testData2 <- window(autoML$dataused,start="2009-01-01",end=end(autoML$dataused))
#P1<-iForecast(Model=autoML,Type="static",newdata=testData2)
#P2<-iForecast(Model=autoML,Type="dynamic",n.ahead=nrow(testData2))

#tail(cbind(testData2[,1],P1))
#tail(cbind(testData2[,1],P2))

#h2o::h2o.shutdown(promp=FALSE) # Remember to shutdown h2o when all works are finished.

iForecast documentation built on June 28, 2025, 5:06 p.m.