Time-series Missing Value Handling using Model — transform.ImputeTS • hana.ml.r

A statistics/model can be obtained when imputing missing values in a given time-series dataset, and this model can be further applied to handling missing values in time-series data of the same structure as the data utilized the model-training phase.

# S3 method for ImputeTS
transform(model, data, key, thread.ratio = NULL)

Arguments

data: DataFrame
DataFrame containing the data for missing value handling using model.
The data must be structured the same as the data used in the training phase.
key: str
Specifies the column name in data that represents the order of time-series.
thread.ratio: double, optional
Controls the proportion of available threads that can be used by this function.
The value range is from 0 to 1, where 0 indicates a single thread, and 1 indicates all available threads.
Values between 0 and 1 will use up to that percentage of available threads.Values outside this range are ignored.
Defaults to 0.

Value

A list of two DataFrames, with names "result" and "stats" respectively:

"result": The input time-series data imputed by model.
"stats": Related statistics for missing value handling, mostly column-wise imputation types and replacement values.

Examples


> imp <- hanaml.ImputeTS(data_train, key = 'ID',
                         imputation_type='most_frequent.linterp')
> outp <- transform(imp, data=data_trans, key= 'ID')
> outp[["result"]]