tts.autoML {iForecast} | R Documentation |
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")
Arguments
y |
The time series object of the target variable, for example, |
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: |
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' |
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.
Value
output |
Output object generated by h2o.automl function of |
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 |
Author(s)
Ho Tsung-wu <tsungwu@ntnu.edu.tw>, 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
# 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))