trendseries (version 1.1.0)

augment_trends: Add trend columns to data frame

Description

Pipe-friendly function that adds trend columns to a tibble or data.frame. Designed for exploratory analysis of monthly and quarterly economic time series. Supports multiple trend extraction methods and handles grouped data.

Usage

augment_trends(
  data,
  date_col = "date",
  value_col = "value",
  group_vars = NULL,
  methods = "stl",
  frequency = NULL,
  suffix = NULL,
  window = NULL,
  smoothing = NULL,
  band = NULL,
  align = NULL,
  params = list(),
  .quiet = FALSE
)

Value

A tibble with original data plus trend columns named trend_{method} or trend_{method}_{suffix} if suffix is provided.

Arguments

data

A data.frame, tibble, or data.table containing the time series data.

date_col

Name of the date column. Defaults to "date". Must be of class Date.

value_col

Name of the value column. Defaults to "value". Must be numeric.

group_vars

Optional grouping variables for multiple time series. Can be a character vector of column names.

methods

Character vector of trend methods. Options: "hp", "bk", "cf", "ma", "stl", "loess", "spline", "poly", "bn", "ucm", "hamilton", "spencer", "ewma", "wma", "triangular", "kernel", "kalman", "median", "gaussian". Default is "stl".

frequency

The frequency of the series. Supports 4 (quarterly) or 12 (monthly). Will be auto-detected if not specified.

suffix

Optional suffix for trend column names. If NULL, uses method names.

window

Unified window/period parameter for moving average methods (ma, wma, triangular, stl, ewma, median, gaussian). Must be positive. If NULL, uses frequency-appropriate defaults. For EWMA, specifies the window size when using TTR's optimized implementation. Cannot be used simultaneously with smoothing for EWMA method.

smoothing

Unified smoothing parameter for smoothing methods (hp, loess, spline, ewma, kernel, kalman). For hp: use large values (1600+) or small values (0-1) that get converted. For EWMA: specifies the alpha parameter (0-1) for traditional exponential smoothing. Cannot be used simultaneously with window for EWMA method. For kernel: multiplier of optimal bandwidth (1.0 = optimal, <1 = less smooth, >1 = more smooth). For kalman: controls the ratio of measurement to process noise (higher = more smoothing). For others: typically 0-1 range.

band

Unified band parameter for bandpass filters (bk, cf). Both values must be positive. Provide as c(low, high) where low/high are periods in quarters, e.g., c(6, 32).

align

Unified alignment parameter for moving average methods (ma, wma, triangular, gaussian). Valid values: "center" (default, uses surrounding values), "right" (causal, uses past values only), "left" (anti-causal, uses future values only). Note: triangular only supports "center" and "right". If NULL, uses "center" as default.

params

Optional list of method-specific parameters for fine control.

.quiet

If TRUE, suppress informational messages.

Details

This function is designed for monthly (frequency = 12) and quarterly (frequency = 4) economic data. It uses economic-appropriate defaults for all trend extraction methods.

For grouped data, the function applies trend extraction to each group separately, maintaining the original data structure while adding trend columns.

Examples

# Simple STL decomposition on quarterly GDP construction data
gdp_construction |> augment_trends(value_col = "index")

# Multiple smoothing methods with unified parameter
gdp_construction |>
  augment_trends(
    value_col = "index",
    methods = c("hp", "loess", "ewma"),
    smoothing = 0.3
  )

# Moving averages with unified window on monthly data
vehicles |>
  tail(60) |>
  augment_trends(
    value_col = "production",
    methods = c("ma", "wma", "triangular"),
    window = 8
  )

# Economic indicators with different methods
ibcbr |>
  tail(48) |>
  augment_trends(
    value_col = "index",
    methods = c("median", "kalman", "kernel"),
    window = 9,
    smoothing = 0.15
  )

# Moving average with right alignment (causal filter)
vehicles |>
  tail(60) |>
  augment_trends(
    value_col = "production",
    methods = "ma",
    window = 12,
    align = "right"
  )

# Advanced: fine-tune specific methods
electric |>
  tail(72) |>
  augment_trends(
    value_col = "consumption",
    methods = "median",
    window = 7
  )