# Getting Started with trendseries

# What is trendseries?

The `trendseries` package helps you extract trends from economic time series data. Trends can be broadly understood as the underlying “direction” of the data, when stripped of its noise and seasonal patterns.

The goal of `trendseries` is to provide a modern, pipe-friendly interface for exploratory analysis of time series data in conventional `data.frame` format. Throughout this vignette, the terms `data.frame` and “data frame” will refer to any dataset in a rectangular format, i.e., `data.frame`/`tibble`/`data.table`.

Most trend extraction methods in R expect `ts` objects, but real-world data typically lives in data frames. Converting back and forth between `ts` and `data.frame` is tedious and error-prone. `trendseries` bridges this gap, letting you work directly with data frames and `tidyverse` tools like `dplyr` and `ggplot2`.

This package was designed with economic time series in mind. It includes methods commonly used in economics (e.g., Hodrick-Prescott filter) as well as general-purpose smoothing methods (e.g., LOESS, moving averages).

# Getting started

`trendseries` revolves around a general wrapper function `augment_trends` that adds new columns to a data frame.

    library(trendseries)
    library(dplyr)
    library(ggplot2)

    theme_series <- theme_minimal(paper = "#fefefe") +
      theme(
        legend.position = "bottom",
        panel.grid.minor = element_blank(),
        # Use colors
        palette.colour.discrete = c(
            "#2c3e50",
            "#e74c3c",
            "#f39c12",
            "#1abc9c",
            "#9b59b6"
        )
      )

This 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() +
      theme_series

To find the trend in data 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.

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

    head(elec_trend)
    #> # A tibble: 6 × 3
    #>   date       consumption trend_stl
    #>   <date>           <dbl>     <dbl>
    #> 1 1979-02-01        1647     1666.
    #> 2 1979-03-01        1736     1688.
    #> 3 1979-04-01        1681     1710.
    #> 4 1979-05-01        1757     1726.
    #> 5 1979-06-01        1689     1743.
    #> 6 1979-07-01        1730     1758.

`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.

    # 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 = 0.8) +
      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 does not produce a color legend.

    ggplot(elec_trend, aes(x = date)) +
      geom_line(
        aes(y = consumption),
        linewidth = 0.8,
        alpha = 0.5,
        color = "#024873FF") +
      geom_line(
        aes(y = trend_stl),
        linewidth = 1,
        color = "#024873FF") +
      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. One or more grouping columns can be selected through the `group_cols` argument.

    cities <- c("Houston", "San Antonio", "Dallas", "Austin")

    txtrend <- txhousing |>
      filter(city %in% cities, year >= 2010) |>
      mutate(date = lubridate::make_date(year, month, 1)) |>
      augment_trends(
        value_col = "median",
        group_cols = "city"
      )

    ggplot(txtrend, aes(date)) +
      geom_line(aes(y = median), alpha = 0.5, color = "#024873FF") +
      geom_line(aes(y = trend_stl), color = "#024873FF") +
      facet_wrap(vars(city)) +
      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 = 0.8, color = "#024873FF") +
      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 = 0.8) +
      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)

`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 `trendseries` compare to the traditional workflow?

The usual workflow 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 to the `ts` object.
3. 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")

## 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.

## Acknowledgements

This package was inspired by the need for a simpler workflow for trend extraction in R. It builds upon many existing packages, including:

- `mFilter` for economic filters.
- `hpfilter` for Hodrick-Prescott filtering.
- `tsbox` for time series conversions.

## Getting Help

If you run into issues:

- Check the documentation: `?augment_trends`
- View examples: `example(augment_trends)`
- Read other vignettes: `vignette(package = "trendseries")`
- Report bugs: GitHub issues