ResidualOutliers: ResidualOutliers

In AdrianAntico/ModelingTools: AutoQuant

ResidualOutliers

Description

ResidualOutliers is an automated time series outlier detection function that utilizes tsoutliers and auto.arima. It looks for five types of outliers: "AO" Additive outliter - a singular extreme outlier that surrounding values aren't affected by; "IO" Innovational outlier - Initial outlier with subsequent anomalous values; "LS" Level shift - An initial outlier with subsequent observations being shifted by some constant on average; "TC" Transient change - initial outlier with lingering effects that dissapate exponentially over time; "SLS" Seasonal level shift - similar to level shift but on a seasonal scale.

Usage

ResidualOutliers(
  data,
  DateColName = NULL,
  TargetColName = NULL,
  PredictedColName = NULL,
  TimeUnit = "day",
  Lags = 5,
  Diff = 1,
  MA = 5,
  SLags = 0,
  SDiff = 1,
  SMA = 0,
  tstat = 2,
  FixedParams = FALSE
)

Arguments

data	the source residuals data.table
DateColName	The name of your data column to use in reference to the target variable
TargetColName	The name of your target variable column
PredictedColName	The name of your predicted value column. If you supply this, you will run anomaly detection of the difference between the target variable and your predicted value. If you leave PredictedColName NULL then you will run anomaly detection over the target variable.
TimeUnit	The time unit of your date column: hour, day, week, month, quarter, year
Lags	the largest lag or moving average (seasonal too) values for the arima fit
Diff	The largest d value for differencing
MA	Max moving average
SLags	Max seasonal lags
SDiff	The largest d value for seasonal differencing
SMA	Max seasonal moving averages
tstat	the t-stat value for tsoutliers
FixedParams	Set to TRUE or FALSE. If TRUE, a stats::Arima() model if fitted with those parameter values. If FALSE, then an auto.arima is built with the parameter values representing the max those values can be.

Value

A named list containing FullData = original data.table with outliers data and ARIMA_MODEL = the arima model object

Author(s)

Adrian Antico

See Also

Other Unsupervised Learning: GenTSAnomVars()

Examples

## Not run: 
data <- data.table::data.table(
  DateTime = as.Date(Sys.time()),
  Target = as.numeric(
    stats::filter(
      rnorm(1000, mean = 50, sd = 20),
      filter=rep(1,10),
      circular=TRUE)))
data[, temp := seq(1:1000)][, DateTime := DateTime - temp][, temp := NULL]
data.table::setorderv(x = data, cols = 'DateTime', 1)
data[, Predicted := as.numeric(
  stats::filter(
    rnorm(1000, mean = 50, sd = 20),
    filter=rep(1,10),
    circular=TRUE))]
Output <- ResidualOutliers(
  data = data,
  DateColName = "DateTime",
  TargetColName = "Target",
  PredictedColName = NULL,
  TimeUnit = "day",
  Lags = 5,
  Diff = 1,
  MA = 5,
  SLags = 0,
  SDiff = 0,
  SMA = 0,
  tstat = 4)
data <- Output[['FullData']]
model <- Output[['ARIMA_MODEL']]
outliers <- data[type != "<NA>"]

## End(Not run)

AdrianAntico/ModelingTools documentation built on Feb. 1, 2024, 7:33 a.m.