iForecast (version 1.1.1)

tts.autoML: Train time series by automatic machine learning of h2o provided by H2O.ai

Description

It generates both the static and recursive time series plots of H2O.ai object generated by package h2o provided by H2O.ai.

Usage

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

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.autoML

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'

Author

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

Details

This function calls the h2o.automl function from package h2o to execute automatic machine learning estimation. When execution finished, it computes two types of time series forecasts: static and recursive. The procedure of h2o.automl automatically generates a lot of time features.

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

# autoML <- tts.autoML(y=dep, x=ind, train.end,arOrder=c(2,4),
# xregOrder=c(0,1,3),type="both")
# 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))