xyz: XYZ analysis

In tsutils: Time Series Exploration, Modelling and Forecasting

XYZ analysis

Description

Perform XYZ analysis on a set of time series.

Usage

xyz(x, m = NULL, prc = c(0.2, 0.3, 0.5), type = c("naive", "ets", "cv"))

Arguments

x	this can either be an array, where each column is a series, or a vector of values. If x is a vector of values forecastability is not calculated and the input is used as such.
m	seasonal length for time series. Required when type is "naive" or "ets".
prc	a vector of percentages indicating how many items are included in each class. By default this is c(0.2,0.3,0.5), but any set of percentage values can be used as long as 0<=prc[i]<=1 and sum(prc)==1.
type	the type of forecastability calculation. This can be: "naive": fit naive and seasonal naive and calculate forecastability using RMSE/mean level. "ets": fit ets and calculate and calculate forecastability using RMSE/mean level. "cv": use coefficient of variation as a proxy of forecastability.

Value

Return object of class abc and contains:

• value: a vector containing the forecastability value of each series.

• class: a vector containing the class membership of each series.

• rank: a vector containing the rank of each series, with 1 being the lowest forecastability series.

• conc: the forecastability concentration of each class, as percentage of total value.

• model: fitted model for each series.

Author(s)

Nikolaos Kourentzes, [e-mail].

References

Ord K., Fildes R., Kourentzes N. (2017) Principles of Business Forecasting, 2e. Wessex Press Publishing Co., p.515-518.

See Also

abc, plot.abc, abcxyz.

Examples

x <- abs(matrix(cumsum(rnorm(5400,0,1)),36,150))
z <- xyz(x,m=12)
print(z)
plot(z)

tsutils documentation built on Nov. 15, 2023, 1:08 a.m.