ffp (version 0.2.2)

exp_decay: Full Information by Exponential Decay

Description

Exponential smoothing twists probabilities by giving relatively more weight to recent observations at an exponential rate.

Usage

exp_decay(x, lambda)
# S3 method for default
exp_decay(x, lambda)
# S3 method for numeric
exp_decay(x, lambda)
# S3 method for matrix
exp_decay(x, lambda)
# S3 method for ts
exp_decay(x, lambda)
# S3 method for xts
exp_decay(x, lambda)
# S3 method for data.frame
exp_decay(x, lambda)
# S3 method for tbl
exp_decay(x, lambda)

Value

A numerical vector of class ffp with the new probabilities distribution.

Arguments

x

An univariate or a multivariate distribution.

lambda

A double for the decay parameter.

Details

The half-life is linked with the lambda parameter as follows:

• HL = log(2) / lambda.

For example: log(2) / 0.0166 is approximately 42. So, a parameter lambda of 0.0166 can be associated with a half-life of two-months (21 * 2).

See Also

crisp kernel_normal half_life

Examples

library(ggplot2)

# long half_life
long_hl <- exp_decay(EuStockMarkets, 0.001)
long_hl
autoplot(long_hl) +
  scale_color_viridis_c()

# short half_life
short_hl <- exp_decay(EuStockMarkets, 0.015)
short_hl
autoplot(short_hl) +
  scale_color_viridis_c()