outlier_cleansing: Apply outlier detection method

In opoyc/sftools: Automate Statistical Forecasting's workflows

Description

Apply outlier detection method

Usage

outlier_cleansing(
  time_series,
  outlier_method,
  data_rule,
  mov_avg_n,
  threshold_val,
  causal_factor = T,
  lag = 4
)

Arguments

time_series	ts object. The signal were to applied the outlier method.
outlier_method	String. Defines the type of outlier method to be applied. Options: *Standard Deviation*, *Iglewicz Hoaglin Method*, *Winsorizing* and *IQR*.
data_rule	String. Defines the classification of the data according to the number of observations. Options: *Historical*, *Moving Average Error* and *Rstl Error*.
mov_avg_n	Integer. Defines how many periods to consider to smooth the series.
threshold_val	Double. Defines the interval cut-off the extreme values. Between $0$ and $1$.
causal_factor	Logical. Defines if the series have regressors or not.
lag	Double. Defines the number of lags to be used for moving average

Value

ts object.

Author(s)

Sze Gee

Examples

## Not run: 
outlier_cleansing()

## End(Not run)

opoyc/sftools documentation built on July 31, 2020, 3:33 a.m.