timetk (version 2.9.0)

ts_clean_vec: Replace Outliers & Missing Values in a Time Series

Description

This is mainly a wrapper for the outlier cleaning function, tsclean(), from the forecast R package. The ts_clean_vec() function includes arguments for applying seasonality to numeric vector (non-ts) via the period argument.

Usage

ts_clean_vec(x, period = 1, lambda = NULL)

Value

A numeric vector with the missing values and/or anomalies transformed to imputed values.

Arguments

x

A numeric vector.

period

A seasonal period to use during the transformation. If period = 1, seasonality is not included and supsmu() is used to fit a trend. If period > 1, a robust STL decomposition is first performed and a linear interpolation is applied to the seasonally adjusted data.

lambda

A box cox transformation parameter. If set to "auto", performs automated lambda selection.

Details

Cleaning Outliers

1. Non-Seasonal (period = 1): Uses stats::supsmu()

2. Seasonal (period > 1): Uses forecast::mstl() with robust = TRUE (robust STL decomposition) for seasonal series.

To estimate missing values and outlier replacements, linear interpolation is used on the (possibly seasonally adjusted) series. See forecast::tsoutliers() for the outlier detection method.

Box Cox Transformation

In many circumstances, a Box Cox transformation can help. Especially if the series is multiplicative meaning the variance grows exponentially. A Box Cox transformation can be automated by setting lambda = "auto" or can be specified by setting lambda = numeric value.

References

• Forecast R Package

• Forecasting Principles & Practices: Dealing with missing values and outliers

See Also

• Box Cox Transformation: box_cox_vec()

• Lag Transformation: lag_vec()

• Differencing Transformation: diff_vec()

• Rolling Window Transformation: slidify_vec()

• Loess Smoothing Transformation: smooth_vec()

• Fourier Series: fourier_vec()

• Missing Value Imputation for Time Series: ts_impute_vec()

• Outlier Cleaning for Time Series: ts_clean_vec()

Examples

library(dplyr)


# --- VECTOR ----

values <- c(1,2,3, 4*2, 5,6,7, NA, 9,10,11, 12*2)
values

# Linear interpolation + Outlier Cleansing
ts_clean_vec(values, period = 1, lambda = NULL)

# Seasonal Interpolation: set period = 4
ts_clean_vec(values, period = 4, lambda = NULL)

# Seasonal Interpolation with Box Cox Transformation (internal)
ts_clean_vec(values, period = 4, lambda = "auto")