clean_ts: Time Series Cleansing

In opoyc/autoforecast: Forecast Development Kit

Description

This function collects the data and applies imputation, time series cuts and leading zeros filtering

Usage

clean_ts(
  .data,
  freq = numeric(),
  winsorize_config = list(),
  imputation_config = list()
)

Arguments

.data	DataFrame or tibble.
freq	numeric, sets the frequency of the data.
winsorize_config	list: options for winsorize cleansing method.
imputation_config	list: options for imputation methods. The names can take the following configurations: impute_method = kalman, median, mean, nearest, interpolation add_transformation = logical, na_regressor = logical (replaces response variable with NA's and imputates) , na_missing_dates = logical (replaces response variable with NA's and imputates when missing dates) , na_value = character (replace NA's by a specific value)

Value

data-frame, tibble or tsibble.

Examples

## Not run: 
clean_ts()

## End(Not run)

opoyc/autoforecast documentation built on May 18, 2021, 1:29 a.m.