Add Trend Features

Generate polynomial trend features for time series data. Trends are computed as sequential indices within each group, with optional polynomial transformations.

Usage

feat_trend(df, date, groups = NULL, degrees = 1L)

Arguments

df

A data frame containing time series data

date

Symbol or character naming the date column (used for sorting)

groups

Character vector naming grouping columns

degrees

Integer vector of polynomial degrees (e.g., c(1, 2) for linear and quadratic)

Value

Data frame with trend features added

Details

Trend features are sequential indices (1, 2, 3, ...) within each group. Polynomial degrees allow capturing non-linear trends:

• degree 1: linear trend (1, 2, 3, ...)

• degree 2: quadratic trend (1, 4, 9, ...)

• degree 3: cubic trend (1, 8, 27, ...)

Examples

if (FALSE) { # \dontrun{
# Add linear trend
df_trend <- feat_trend(df, date = date, groups = "store_id", degrees = 1)

# Add linear and quadratic trends
df_trend <- feat_trend(df, date = date, groups = "store_id", degrees = c(1, 2))
} # }