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Changelog

Source: NEWS.md

trendseries 1.6.0

  • augment_trends(), augment_rolling(), and decompose_series() now handle date-column names that overlap generated column names, preserving the date column and applying the usual numeric suffix to the generated column.

  • extract_trends() and augment_trends() now apply the first window to methods such as WMA in mixed vector-window requests, with a warning, instead of silently using the default window.

  • extract_trends() and augment_trends() now honor Kalman smoothing as the measurement-to-process noise ratio and preserve individually supplied noise variances. Explicitly supplying both variances takes precedence over the ratio.

  • extract_trends() and augment_trends() now report STL and UCM estimator fallbacks even with .quiet = TRUE. Quiet augmentation also consolidates warnings and identifies affected groups.

  • augment_trends(), augment_rolling(), decompose_series(), and index_series() now keep groups distinct when their labels contain periods or combine missing values with the literal string "NA".

  • augment_trends() and decompose_series() now reject interior missing values in daily and weekly series instead of silently removing those observations before estimation. Leading and trailing missing values and irregular trading calendars remain supported.

  • augment_rolling() now warns about every group whose year-to-date accumulation starts mid-year.

  • index_series() now detects frequency independently within each group, including groups with different dating conventions or frequencies.

  • Centered even-window moving averages now return padded NA values when the series cannot support the N+1 filter weights.

  • roll_series() and augment_rolling() now honor na_rm = TRUE for centered even-window means by renormalizing the observed weights while retaining boundary padding.

  • Fixed augment_trends(), augment_rolling(), decompose_series(), deseason_series(), and detrend_series() returning rows in join or group order rather than preserving the caller’s input order.

  • Fixed augment_trends() dropping the warnings raised by the filter it dispatched to. A fallback to another estimator, such as a failed UCM fit or STL on a non-seasonal series, now reaches the caller, along with the group it came from. A warning raised for several groups is reported once.

  • Fixed augment_trends(), augment_rolling(), and decompose_series() dropping rows whose grouping column is NA. Those rows are now treated as one more series and returned with the rest.

Irregular and daily series

  • Fixed augment_trends(), augment_rolling(), and decompose_series() returning an all-NA column for daily and weekly series. Results were converted back to a data frame through the ts time index, which advances by 1/252 per observation while a daily calendar skips weekends and holidays. The regenerated dates therefore drifted from the real ones, and the join back onto the input matched nothing. Results now carry the dates the series was built from, so they rejoin the rows they were computed from.

  • Fixed augment_trends() and augment_rolling() duplicating rows for semi-annual data. The merge key floored dates to the calendar unit, mapping any frequency other than 12 or 4 to the year, which put both halves of a year on one key. The key now follows the frequency.

  • Fixed window = "ytd" resetting off-calendar for daily and weekly series. The year came from the ts time index, which advances a year every frequency observations, so the reset drifted further from January each year. Year-to-date accumulations now reset on the calendar year. A ts passed directly to roll_series() carries no dates, so "ytd" is rejected there for those frequencies.

  • augment_trends() and augment_rolling() now reject a repeated date in a daily or weekly series. Two rows cannot occupy one position, and results are matched back by date.

  • Rebuilt the trend_ma column of coffee_arabica and coffee_robusta, which was NA for every row because the datasets were generated while the join above was broken. The column now holds the 22-observation right-aligned moving average its documentation describes.

Indexing

  • New index_series() rescales one or more data-frame series to a configurable base value, using either the earliest observation or the mean over a year or date range, with support for grouped data and multiple value columns.

Centered moving averages

  • Fixed the placement of the 2xN moving average used by extract_trends() and augment_trends() when methods = "ma" is called with an even window and align = "center". The filter weights were correct but sat one period early, so the trend led the series by one month for a monthly 2x12 and by one quarter for a quarterly 2x4. Composing two centered RcppRoll passes caused this: for an even window, align = "center" places n / 2 observations after the anchor and only n / 2 - 1 before it, and the offset survives the second pass. The 2xN filter is now applied directly as the symmetric weights c(1/2, 1, ..., 1, 1/2) / N, which also pads N / 2 positions at each end instead of N / 2 - 1 at the start and N / 2 + 1 at the end. Odd windows and non-centered alignments are unaffected, as are the other moving average methods.

  • roll_series() and augment_rolling() apply the same 2xN filter for stats = "mean" with an even window and align = "center", so the rolling mean and the ma trend method agree given the same window and alignment. The other statistics have no such correction and use a window with one extra period after the anchor.

Rolling aggregations

  • Fixed rolling and year-to-date aggregations returning a backend identity value for a window with no observed values under na_rm = TRUE. A "sum" returned 0, a "chain" returned 0, a "min" returned Inf, a "max" returned -Inf, a "mean" returned NaN, and an "sd" returned 0. All six now return NA, as does "sd" for a window holding a single value.

  • roll_series() and augment_rolling() now warn when a year-to-date accumulation starts mid-year. The first year of a series beginning in, say, July accumulates from July rather than from January, so it is not comparable with the years that follow. The values are unchanged.

  • Fixed the chain scale warning never reaching a grouped augment_rolling() call. The warning was gated on .quiet, which the grouped path sets for every group. .quiet now suppresses progress messages only. The scale and calendar checks run once for the whole call rather than once per group.

  • window = "ytd" now requires a seasonal frequency. Annual data previously returned the series unchanged, since each year holds one observation.

  • roll_series() and augment_rolling() now warn about arguments the requested combination ignores: align under window = "ytd", and percent = TRUE without stats = "chain".

  • A grouped augment_rolling() call with a window longer than some group now names every group that is too short, with its number of rows. The error previously came from whichever group failed first and named none of them.

Documentation

  • Documented that augment_rolling() preserves the caller’s input row order.

  • Reorganized the pkgdown articles and package vignettes. The Trend Extraction Methods catalogue now lives on the documentation site as a pkgdown article.

  • Updated vignette and article plots to use the ekioplot visual identity. The package is listed in Suggests and is used only when building the vignettes.

trendseries 1.5.0

Rolling and year-to-date aggregations

  • New augment_rolling() and roll_series() add rolling and year-to-date aggregations, mirroring the augment_trends() / extract_trends() pair. augment_rolling() takes a data frame and adds roll_{stat}_{window} columns (e.g. roll_sum_12); roll_series() takes a ts, xts, or zoo object and returns ts results. Six statistics are available: "sum", "chain", "mean", "sd", "min", and "max".

  • stats = "chain" calculates prod(1 + r) - 1 which assumes the series is a rate, e.g., monthly inflation rate. Use percent = TRUE when rates are in percentage points; a warning is issued when the values look mis-scaled for the declared setting.

  • window = "ytd" computes an expanding year-to-date accumulation that resets each January, for any of the six statistics.

  • align defaults to "right", the convention for accumulated economic indicators, rather than the centered default used for trends.

  • Grouped series are supported via group_cols, and multiple value_col entries are suffixed with the column name.

  • Rolling statistics are kept in a registry separate from the trend methods. A rolling sum is not in the units of the series, so it is not a trend and cannot be passed to detrend_series().

Missing values and period grids

Series with gaps were previously handled differently by each entry point, and the disagreements were silent. Missing value handling is now one policy, applied everywhere: a gap inside the observed span is rejected, and missing values at the edges are excluded from estimation rather than rejected.

  • augment_trends(), decompose_series(), deseason_series(), and detrend_series() no longer return silently misdated results for series with interior gaps. These functions assumed the series had no gaps. Because observations are positioned by period, a missing period shifted every later value one slot earlier, so results merged back onto the wrong dates and the series lost its final period. Interior gaps and duplicated periods are now rejected, whether the gap comes from a row with a missing value or from a period absent from the data.

  • extract_trends() now rejects missing values inside the observed span of a ts, xts, or zoo input. Previously they were passed straight to the filters, where the outcome depended on the method and was usually silent: stl, spline, and hamilton raised an error, hp, bk, and cf returned an all-NA series, and the recursive methods (ewma, bn) propagated the gap to every later observation. Impute the gaps before extracting a trend.

  • Leading and trailing missing values continue to work. extract_trends() excludes them from estimation and returns the result on the time base of the input, with NA for the periods that were never observed.

  • augment_rolling() and roll_series() are the exception, by design. A rolling window has well-defined local semantics for a gap, so rows with missing values keep their calendar position and na_rm controls whether an affected window yields NA or is computed from the observations present.

  • df_to_ts() was the last entry point still building a misdated series from a gapped input, and it now applies the same check. A missing or duplicated period is rejected instead of shifting every later observation one slot earlier. Rows are also sorted before conversion, and a row with no date is dropped with a warning rather than left in place to occupy a period it cannot be positioned in. Missing values are kept, which is what leaves the series correctly dated.

  • df_to_ts() now counts the starting period in units of the frequency. It previously used the calendar month whatever the frequency, so a quarterly series beginning in April started at Q4, an annual series dated March started two years late, and a semiannual series beginning in July started at H1. Monthly series were unaffected.

  • Series whose frequency has no exact calendar period (weekly, daily) are not grid-checked. Their starting period is now placed proportionally within the year rather than defaulting to the first period.

Empty input

Documentation

  • Function documentation has been tightened, and the missing value policy is now stated on the arguments it applies to.

  • Reworded the README, the vignettes, and the help pages: cut the fixed method counts that go stale on each release, replaced promises that the components sum back exactly with what the functions do, and removed the duplicated sections in Getting Started.

  • Added "henderson" to the documented methods options of augment_trends() and extract_trends(). The method has always been accepted, but the help pages listed the other nineteen.

trendseries 1.4.0

CRAN release: 2026-07-14

This release combines the 1.3.0 development series, which was never published on CRAN, with the 1.4.0 changes.

New Features

  • decompose_series() is now exported and available for use. This pipe-friendly function decomposes a time series into trend, seasonal, and remainder components, adding trend_*, seasonal_*, and remainder_* columns to the input data frame. Five methods are available: "stl" (default), "regression", "classic" (classical decomposition via centred moving averages, stats::decompose()), "bsm" (Basic Structural state-space Model estimated by the Kalman smoother, stats::StructTS()), and "seats" (X-13ARIMA-SEATS via the optional seasonal package, a Suggested dependency only required for this method). It supports grouped decomposition via group_cols, and the components add back up to the original series. See the new Decomposing Series vignette. Additional conveniences:

    • methods accepts a vector (e.g. c("stl", "classic")), adding each method’s components as separate columns for side-by-side comparison.
    • transform = "log" provides a uniform multiplicative decomposition across every method (decompose on the log scale, exponentiate back).
    • seasadj = TRUE adds a seasadj_{method} column with the seasonally adjusted series.

  • deseason_series() is a new convenience wrapper around decompose_series() focused on seasonal adjustment. It adds a seasadj_{method} column with the deseasoned series (methods "stl" or "seats"), and optionally the full trend/seasonal/remainder decomposition via components = TRUE.

  • detrend_series() is a new convenience wrapper around augment_trends() that returns the detrended series, i.e. the deviation from the trend (the cycle in economics). transform = "log" returns the log deviation from trend (approximately the percentage deviation, the output-gap convention), and components = TRUE also keeps the fitted trend_{method} columns.

Bug Fixes

  • The Hamilton filter now uses frequency-aware default parameters, as documented. The defaults were hardcoded to the quarterly values (h = 8, p = 4) regardless of frequency, so monthly series were filtered with a two-quarter horizon instead of the recommended two-year one. Monthly data now defaults to h = 24, p = 12 (Hamilton 2018); quarterly behaviour is unchanged. Because the monthly defaults are larger, monthly series now require at least 37 observations (h + p + 1) and the first 35 trend values are NA (previously 13 and 11). Pass params = list(hamilton_h = , hamilton_p = ) to reproduce old results.

Internal Improvements

  • Removed the glue dependency. The two remaining glue::glue() calls were replaced by the interpolation cli already provides.

  • Removed dead internal code left over from earlier refactors: the unused .ensure_odd_window() and .check_deprecated_params() helpers, leftover zlema references, and stale HoltWinters/roll_median namespace imports.

  • The list of valid methods is now defined in a single internal registry, which augment_trends() and extract_trends() both read from. The valid decomposition methods for decompose_series() are defined there as well.

  • The unified parameter validation (window, smoothing, band, align, params) shared by augment_trends() and extract_trends() now lives in a single internal helper, shared by both functions.

Documentation

  • Added a Trend Extraction Methods vignette cataloguing all 20 trend methods by family.

  • Added a Detrending Series vignette covering detrend_series(): the deseason-then-detrend workflow for seasonal data, percentage deviations from trend via transform = "log", method comparison (HP vs Hamilton), and grouped detrending.

  • Removed outdated references to the TTR package from the augment_trends() and extract_trends() documentation. The EWMA window parameter is now documented by what it does: it sets alpha = 2 / (window + 1).

trendseries 1.2.0

CRAN release: 2026-05-02

Breaking Changes

  • The group_vars argument in augment_trends() is deprecated in favour of group_cols, and calls with group_vars now issue a deprecation warning. Replace group_vars = ... with group_cols = ...; group_vars will be removed in a future release.

New Features

  • augment_trends() accepts multiple value columns through a character vector in value_col. Trends are extracted for each column and named trend_{method}_{col} (e.g. trend_stl_consumption).

  • The UCM trend estimator now uses fixed variance components with signal-to-noise ratios derived from Hodrick-Prescott filter lambdas, producing smoother trends by default. The smoothing parameter overrides the default.

  • Added two Transport for London datasets: transit_london_monthly, monthly totals of reported bus and Tube journeys, and transit_london_avgs, monthly averages of the reported daily journey counts.

Bug Fixes and Improvements

  • Updated the vignettes to use group_cols in place of the deprecated group_vars.
  • Fixed typos, grammar, and mislabeled plot axes across the vignettes.
  • Removed a stale ZLEMA reference from the moving average documentation.

trendseries 1.1.0

CRAN release: 2025-11-12

Release Date: November 2025

Breaking Changes

  • Removed Butterworth and Savitzky-Golay filters: The Butterworth low-pass filter and the Savitzky-Golay polynomial smoothing have been removed to focus the package on core econometric methods. The signal package dependency has been removed.

  • Removed exponential smoothing methods: Simple and double exponential smoothing (exp_simple, exp_double) have been removed. Users can continue using EWMA for exponential smoothing. The forecast package dependency has been removed.

Note

  • EWMA retained: The Exponentially Weighted Moving Average (EWMA) method has been kept as it is categorized as a moving average method and is widely used in economic analysis.

trendseries 1.0.1

Release Date: January 2025

Breaking Changes

  • Removed ZLEMA filter: The Zero-Lag Exponential Moving Average (ZLEMA) has been removed from the package. This method was not commonly used in economic analysis and added unnecessary complexity. Users needing zero-lag smoothing can use EWMA with appropriate alpha values.

New Features

  • Added Spencer filter: Classic 15-term Spencer moving average filter for trend extraction. The Spencer filter uses symmetric weights designed to preserve cubic polynomial trends while providing smooth results. Implementation uses linear extrapolation at endpoints for simplicity and computational efficiency.

Bug Fixes and Improvements

Moving Average Enhancements

  • Implemented econometrically correct 2xN MA for centered even-window moving averages:
    • When using centered alignment with even windows (e.g., 12 for monthly data, 4 for quarterly), the simple moving average now automatically applies the proper 2xN double-smoothing technique
    • This matches the X-13ARIMA-SEATS standard for seasonal adjustment
    • Example: window=12, align="center" now correctly applies a 2x12 MA instead of naive centering
    • Non-centered alignments (right/left) and odd windows continue to use regular single MA
  • Fixed misleading “2x” notation in messages:
    • Previous versions displayed “2x12” in messages but didn’t actually implement double smoothing
    • Now the “2x” notation only appears when the 2xN algorithm is actually used
    • Messages clearly indicate: “Computing 2x12-period MA (auto-adjusted for even-window centering)” vs “Computing 12-period MA with right alignment”
  • Added comprehensive tests for 2xN MA:
    • 5 new test cases validating correct behavior for monthly and quarterly data
    • Tests confirm 2xN MA differs from simple MA for even-window centered cases
    • All 106 MA filter tests passing

Technical Changes

  • Added glue package to Imports for message formatting
  • Added .ma_2x() internal function implementing proper double-smoothing
  • Added .ensure_odd_window() utility function for future use
  • Updated test expectations to account for new 2xN behavior

Impact

This is an important correctness fix for users doing seasonal adjustment or business cycle analysis with monthly/quarterly data. The new implementation ensures that centered moving averages with even windows produce econometrically sound results.

trendseries 1.0.0

The first production release of trendseries, an R package for extracting trends from economic time series.

Trend extraction

Two functions cover the main workflows. augment_trends() takes a data frame and adds one trend_{method} column per requested method, with grouped operations through dplyr. extract_trends() takes ts, xts, or zoo objects and returns ts results.

Four families cover the methods in this release.

  • Econometric filters: HP (one-sided and two-sided), Baxter-King, Christiano-Fitzgerald, Hamilton, Beveridge-Nelson, and a state-space UCM.
  • Moving averages: simple, weighted, exponential, zero-lag, triangular, median, and Gaussian.
  • Smoothing: STL, loess, splines, polynomial trends, and simple and double exponential smoothing.
  • Signal processing: Kalman smoother, Savitzky-Golay, Butterworth, and kernel smoothing.

Both functions share one parameter system across every method, with window, smoothing, band, align, and params. Defaults track the series frequency, so the HP filter sets lambda to 1600 for quarterly and 14400 for monthly data, moving averages default to four-quarter or twelve-month windows, and bandpass filters use the 6-to-32-quarter business cycle range. Monthly and quarterly series are the main target; STL and the moving average methods also run on daily and other frequencies.

Datasets

Ten economic datasets ship with the package.

  • Brazilian data (BCB): gdp_construction, ibcbr, vehicles, oil_derivatives, and electric.
  • UK data (ONS): retail_households and retail_autofuel.
  • Coffee prices (CEPEA): coffee_arabica and coffee_robusta, both daily.
  • Metadata: series_metadata.

Installation

# install.packages("devtools")
devtools::install_github("viniciusoike/trendseries")

Quick example

library(trendseries)

gdp_construction |>
  augment_trends(value_col = "index", methods = c("hp", "bk", "ma"))

Built on mFilter, hpfilter, RcppRoll, forecast, dlm, signal, and tsbox. MIT license; requires R 4.1.0 or later.