Gaps and outliers

fill_gaps() fills missing values, following the seasonal pattern when there is one. find_outliers() finds the observations far from what the trend and the season suggest, and what they should rather be. clean_series() does both.

Usage

fill_gaps(y, period = NULL)

find_outliers(y, period = NULL)

clean_series(y, period = NULL)

Arguments

y

A ts or a numeric vector, possibly with NA.

period

The seasonal period, when y is a plain vector.

Value

fill_gaps() and clean_series(): the series, a ts when y is one. find_outliers(): a tibble with the index (from 1), the value and its replacement, plus the time for a ts (a date for monthly and quarterly data).

Examples

y <- log(AirPassengers)
y[c(30, 100)] <- y[c(30, 100)] + c(0.8, -0.7)
y[60] <- NA
find_outliers(y)
#> # A tibble: 5 × 4
#>   index time       value replacement
#>   <int> <date>     <dbl>       <dbl>
#> 1    30 1951-06-01  5.98        5.22
#> 2    52 1953-04-01  5.46        5.43
#> 3    62 1954-02-01  5.24        5.30
#> 4   100 1957-04-01  5.15        5.85
#> 5   135 1960-03-01  6.04        6.14
clean_series(y)[c(30, 60, 100)]
#> [1] 5.224249 5.300749 5.853691