# Augmenting Trends

`augment_trends()` is the core function of `trendseries`: it adds one or more `trend_{method}` columns to a data frame, estimating the underlying “direction” of a time series once noise and seasonal patterns are stripped away. This vignette walks through the interface in detail: a first single-method example, grouped and multi-method extraction, and fine control over method-specific parameters.

    library(trendseries)
    # Optional
    library(dplyr)
    library(tidyr, include.only = "pivot_longer")

Note that `dplyr` isn’t required for `trendseries` to work. In fact, `trendseries` should work with any `data.frame`-type object.

The settings below are only defined for aesthetic purposes and can be ignored.

    library(ggplot2)

    theme_series <- theme_minimal(paper = "#fefefe") +
      theme_sub_panel(grid.minor = element_blank()) +
      theme_sub_plot(margin = margin(10, 10, 10, 10)) +
      theme_sub_axis_x(
        line = element_line(color = "gray20"),
        ticks = element_line(color = "gray20", linewidth = 0.35),
        title = element_blank()
      ) +
      theme(
        legend.position = "bottom",
        # Use colors
        palette.colour.discrete = c(
          "#2c3e50",
          "#e74c3c",
          "#f39c12",
          "#1abc9c",
          "#9b59b6"
        )
      )

## Trend extraction vs decomposition and detrending

It is worth being clear about what `augment_trends()` does and does not do.

- `augment_trends()` returns only the trend (`trend_*` columns): a single smooth component, with the seasonal and irregular movements simply smoothed away.
- `decompose_series()` builds on the same engine but returns all three components — trend, seasonal, and remainder — that add back up exactly to the original series. See the Decomposing Series vignette.
- `detrend_series()` is the mirror image: it fits the trend with `augment_trends()` and then subtracts it, returning the deviation from trend (the cycle). See the Detrending Series vignette.

This vignette focuses on `augment_trends()` itself: the pipe-friendly data frame interface, its `ts`/`xts`/`zoo` counterpart `extract_trends()`, and the shared parameter system both use.

## A first trend

`trendseries` comes with some useful datasets, some of which will be presented in this vignette. The `electric` dataset contains monthly electric consumption for Brazilian households from 1979 to 2025.

    head(electric)
    #> # A tibble: 6 × 2
    #>   date       consumption
    #>   <date>           <dbl>
    #> 1 1979-02-01        1647
    #> 2 1979-03-01        1736
    #> 3 1979-04-01        1681
    #> 4 1979-05-01        1757
    #> 5 1979-06-01        1689
    #> 6 1979-07-01        1730

    ggplot(electric, aes(date, consumption)) +
      geom_line(lwd = 0.7) +
      theme_series

To estimate the trend we use `augment_trends` and select a method: in this case, STL (see `stats::stl`). The `date_col` (default `"date"`) and `value_col` (default `"value"`) arguments identify the relevant columns. The result is appended as a column named `trend_{method}` such as “trend_stl”, “trend_ma” (for a Moving Average), “trend_median” (for a Moving Median), etc.

    elec_trend <- augment_trends(
      electric,
      date_col = "date",
      value_col = "consumption",
      methods = "stl"
    )

    head(elec_trend)
    #> # A tibble: 6 × 3
    #>   date       consumption trend_stl
    #>   <date>           <dbl>     <dbl>
    #> 1 1979-02-01        1647     1675.
    #> 2 1979-03-01        1736     1695.
    #> 3 1979-04-01        1681     1716.
    #> 4 1979-05-01        1757     1731.
    #> 5 1979-06-01        1689     1747.
    #> 6 1979-07-01        1730     1761.

`augment_trends` will do its best to try to infer the appropriate frequency but this information can be supplied manually.

    elec_trend <- augment_trends(
      electric,
      date_col = "date",
      value_col = "consumption",
      methods = "stl",
      frequency = 12
    )

There are two options to visualize the data using `ggplot2`. The first is to convert the data to a “long” format and define a “name” for each of the series.

    # Prepare data for plotting
    plot_data <- elec_trend |>
      tidyr::pivot_longer(
        cols = -date,
        names_to = "series",
        values_to = "value"
      ) |>
      mutate(
        series = case_when(
          series == "consumption" ~ "Data (original)",
          series == "trend_stl" ~ "Trend (STL)"
        )
      )

    # Create the plot
    ggplot(plot_data, aes(x = date, y = value, color = series)) +
      geom_line(linewidth = 0.7) +
      labs(
        title = "Residential Electricity Consumption",
        x = NULL,
        y = "Electric Consumption (GWh)",
        color = NULL
      ) +
      theme_series

An alternative is to add the trend as an additional `geom_line` layer. This is quicker but doesn’t scale as well.

    ggplot(elec_trend, aes(x = date)) +
      geom_line(
        aes(y = consumption, color = "Original"),
        linewidth = 0.7,
        alpha = 0.5
      ) +
      geom_line(
        aes(y = trend_stl, color = "Trend (STL)"),
        linewidth = 1
      ) +
      scale_color_manual(values = c("#1E3A5F", "#1E3A5F")) +
      labs(
        title = "Residential Electricity Consumption",
        subtitle = "Decomposition using an STL trend",
        x = NULL,
        y = "Electric Consumption (GWh)",
        color = NULL
      ) +
      theme_series

## Multiple time series

`trendseries` makes it easy to compute trends across several series at once. One or more grouping columns can be selected through the `group_cols` argument. Note that this works best for datasets in a “tidy” (long) format. Here we use `electricity`, which records monthly electricity consumption for three sectors (residential, commercial, and industrial).

    elec_sub_trend <- electricity |>
      dplyr::filter(date >= as.Date("1995-01-01")) |>
      augment_trends(
        date_col = "date",
        value_col = "value",
        group_cols = "name_series",
        methods = "stl"
      )

    ggplot(elec_sub_trend, aes(date)) +
      geom_line(aes(y = value), alpha = 0.5, color = "#1E3A5F") +
      geom_line(aes(y = trend_stl), color = "#1E3A5F") +
      facet_wrap(vars(name_series), ncol = 1) +
      theme_series

## Multiple trend methods

`trendseries` also facilitates extracting trends with different methods simultaneously. The next example uses a chained index of retail sales of automotive fuel in the UK. The original data comes from the UK Office for National Statistics.

    ggplot(retail_autofuel, aes(date, value)) +
      geom_line(lwd = 0.7, color = "#1E3A5F") +
      theme_series

This example also highlights how `augment_trends` fits neatly in a pipe workflow.

    fuel_trends <- retail_autofuel |>
      filter(date >= as.Date("2012-01-01")) |>
      augment_trends(
        methods = c("stl", "hp", "loess")
      )

    comparison_plot <- fuel_trends |>
      tidyr::pivot_longer(
        cols = c(value, starts_with("trend_")),
        names_to = "method",
      ) |>
      mutate(
        method = case_when(
          method == "value" ~ "Data (original)",
          method == "trend_hp" ~ "HP Filter",
          method == "trend_stl" ~ "STL",
          method == "trend_loess" ~ "LOESS"
        )
      )

    ggplot(comparison_plot, aes(x = date, y = value, color = method)) +
      geom_line(linewidth = 0.7) +
      labs(
        title = "Comparing Different Trend Extraction Methods",
        subtitle = "Same data, different methods",
        x = "Date",
        y = "Retail Sales Index",
        color = "Method"
      ) +
      theme_series

## Finer control

Filter-extraction methods are spread across different packages and thus use different conventions for parameter names. `trendseries` tries to simplify this when possible. Methods like moving averages and moving medians have a shared “window” argument that defines the size of the rolling window.

    elec_trends <- electric |>
      rename(value = consumption) |>
      # window controls the s.window argument by default
      augment_trends(methods = "stl", window = 17) |>
      # Creates a 11-month moving median
      augment_trends(methods = "median", window = 11) |>
      # Creates a (centered) 5-month moving average
      augment_trends(methods = "ma", window = 5) |>
      # Creates a (centered) 2x12 moving average
      augment_trends(methods = "ma", window = 12)

    comparison_plot <- elec_trends |>
      tidyr::pivot_longer(
        cols = c(value, starts_with("trend_")),
        names_to = "method",
      ) |>
      mutate(
        method = case_when(
          method == "value" ~ "Data (original)",
          method == "trend_median" ~ "Median",
          method == "trend_stl" ~ "STL",
          method == "trend_ma" ~ "MA (5)",
          method == "trend_ma_1" ~ "MA (2x12)"
        )
      ) |>
      filter(date >= as.Date("2018-01-01"))

    ggplot(comparison_plot, aes(x = date, y = value, color = method)) +
      geom_line(linewidth = 0.7) +
      labs(
        title = "Comparing Different Trend Extraction Methods",
        subtitle = "Same data, different methods",
        x = "Date",
        y = "Retail Sales Index",
        color = "Method"
      ) +
      theme_series

Note that `trendseries` simplifies trend extraction at the cost of some precision. For instance, `stats::stl` has both a `t.window` and an `s.window` argument. The `window` argument in `trendseries` controls `s.window` by default — an opinionated choice that favors simplicity.

## How does `augment_trends()` compare to the traditional workflow?

The typical workflow of estimating trends from a single series involves:

1. Converting pairs of `date` and `numeric` columns to `ts` objects. This usually means manually inputting both `frequency` and `start` parameters.
2. Applying a filter function to the `ts` object.
3. Extracting the trend. Since each filtering function returns a different type of object the complexity varies. For example `stats::stl` requires `.$time.series[, "trend"]` and returns a `ts` object.
4. Converting the `ts` object back to the original `data.frame`.

This can be cumbersome, especially when working with multiple series or grouped data. Merging back the results with the original data can also be error-prone due to misalignment of dates and additional `NA` values introduced by some filters.

For instance, consider estimating a HP filter on `gdp_construction`. The first step requires converting the data frame to a `ts` object, manually inputting both `frequency` and `start` parameters.

    gdp_cons <- ts(
      gdp_construction$index,
      frequency = 4,
      start = c(1996, 1)
    )

    # Or, using lubridate to extract year and month
    gdp_cons <- ts(
      gdp_construction$index,
      frequency = 4,
      start = c(
        lubridate::year(min(gdp_construction$date)),
        lubridate::quarter(min(gdp_construction$date))
      )
    )

Then applying the HP filter using the `mFilter` package.

    gdp_trend_hp <- mFilter::hpfilter(gdp_cons, 1600)

And finally, converting it back to a `data.frame` and merging it with the original data.

    # Convert back to data frame using tsbox
    trend_df <- tsbox::ts_df(gdp_trend_hp$trend)
    names(trend_df) <- c("date", "trend_hp")

    # Join with original data
    gdp_manual <- left_join(gdp_construction, trend_df, by = "date")

`augment_trends()` collapses all four steps above into a single call:

    gdp_auto <- augment_trends(gdp_construction, value_col = "index", methods = "hp")

## What are the alternatives to `trendseries`?

The closest alternative to `trendseries` is the `tsibble`/`fable` ecosystem, which provides a `model()` function for applying models — including some trend extraction methods — to grouped time series. Like `trendseries`, these packages integrate well with `tidyverse` tools and pipes.

However, `fable` was designed primarily for forecasting, which means its trend extraction capabilities are more limited. They also lack some popular methods commonly used by economists, such as the HP filter and the Hamilton filter.

Additionally, these packages require using the `tsibble` data structure, which pulls users away from the familiar `data.frame`/`tibble` format. For users working with just a few time series and relying on R’s built-in `ts` functionality, the `tsibble` structure can feel unnecessarily complex.

## Summary

- `augment_trends()` adds `trend_{method}` columns to a `data.frame` and is the core function of `trendseries`; `extract_trends()` is the equivalent for `ts`/`xts`/`zoo` objects.
- `group_cols` extracts trends for several series at once from tidy (long) data; `methods` accepts a vector to compare several methods side by side.
- The unified parameters (`window`, `smoothing`, `band`, `align`, `params`) give consistent, if slightly opinionated, control over method-specific options across all 20 supported methods.
- `decompose_series()` and `detrend_series()` build on the same engine to return, respectively, the full trend/seasonal/remainder split and the deviation from trend — see the Decomposing Series and Detrending Series vignettes.