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.