The log_interval_vec() transformation constrains a forecast to an interval
specified by an upper_limit and a lower_limit. The transformation provides
similar benefits to log() transformation, while ensuring the inverted transformation
stays within an upper and lower limit.
log_interval_vec(
x,
limit_lower = "auto",
limit_upper = "auto",
offset = 0,
silent = FALSE
)log_interval_inv_vec(x, limit_lower, limit_upper, offset = 0)
A numeric vector of the transformed series.
A positive numeric vector.
A lower limit. Must be less than the minimum value. If set to "auto", selects zero.
An upper limit. Must be greater than the maximum value. If set to "auto", selects a value that is 10% greater than the maximum value.
An offset to include in the log transformation. Useful when the data contains values less than or equal to zero.
Whether or not to report the parameter selections as a message.
Log Interval Transformation
The Log Interval Transformation constrains values to specified upper and lower limits. The transformation maps limits to a function:
log(((x + offset) - a)/(b - (x + offset)))
where a is the lower limit and b is the upper limit
Inverse Transformation
The inverse transformation:
(b-a)*(exp(x)) / (1 + exp(x)) + a - offset
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 & Anomaly Cleaning for Time Series: ts_impute_vec(), ts_clean_vec()
Other common transformations to reduce variance: log(), log1p() and sqrt()
library(dplyr)
values_trans <- log_interval_vec(1:10, limit_lower = 0, limit_upper = 11)
values_trans
values_trans_forecast <- c(values_trans, 3.4, 4.4, 5.4)
values_trans_forecast %>%
log_interval_inv_vec(limit_lower = 0, limit_upper = 11) %>%
plot()
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