cal_features: Calculate features for new time series instances

In seer: Feature-Based Forecast Model Selection

Calculate features for new time series instances

Description

Computes relevant time series features before applying them to the model

Usage

cal_features(
  tslist,
  seasonal = FALSE,
  m = 1,
  lagmax = 2L,
  database,
  h,
  highfreq
)

Arguments

tslist	a list of univariate time series
seasonal	if FALSE, restricts to features suitable for non-seasonal data
m	frequency of the time series or minimum frequency in the case of msts objects
lagmax	maximum lag at which to calculate the acf (quarterly series-5L, monthly-13L, weekly-53L, daily-8L, hourly-25L)
database	whether the time series is from mcomp or other
h	forecast horizon
highfreq	whether the time series is weekly, daily or hourly

Value

dataframe: each column represent a feature and each row represent a time series

Author(s)

Thiyanga Talagala

seer documentation built on Oct. 1, 2022, 9:06 a.m.