trendseries 1.2.0

New Features

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

• Improved UCM (Unobserved Components Model) trend extraction. The model now uses fixed variance components with signal-to-noise ratios derived from Hodrick-Prescott filter lambdas, producing smoother, economically meaningful trends by default. The smoothing parameter can be used to override the default.

• Added London Underground transit datasets: transit_london_monthly and transit_london_avgs.

Bug Fixes and Improvements

• The group_vars argument in augment_trends() is deprecated in favour of group_cols. A deprecation warning is now issued when group_vars is used. group_vars will be removed in a future release.

• Fixed typos, grammar, and prose across vignettes.

• Updated vignettes to use group_cols instead of deprecated group_vars.

• Fixed mislabeled y-axis in vignette plots.

• Removed stale ZLEMA reference from moving average documentation.

trendseries 1.1.0

Release Date: November 2025

Breaking Changes

• Removed Butterworth filter: The Butterworth low-pass filter has been removed to focus the package on core econometric methods. The signal package dependency has been removed.

• Removed Savitzky-Golay filter: The Savitzky-Golay polynomial smoothing filter has been removed to streamline the package. 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

Release Date: January 2025

First Production Release

This is the first production release of trendseries, providing a modern, pipe-friendly interface for extracting trends from economic time series data.

Key Features

• 21 Trend Extraction Methods:

  • Econometric filters: HP filter (one-sided and two-sided), Baxter-King, Christiano-Fitzgerald, Hamilton filter, Beveridge-Nelson decomposition, Unobserved Components Model (UCM)
  • Moving averages: Simple (SMA), Weighted (WMA), Exponential (EWMA), Zero-lag (ZLEMA), Triangular, Median, Gaussian-weighted
  • Smoothing methods: STL decomposition, Loess, Splines, Polynomial trends, Simple/Double exponential smoothing
  • Signal processing: Kalman filter/smoother, Savitzky-Golay, Butterworth, Kernel smoother

• Two-Function API:

  • augment_trends(): Pipe-friendly function for tibble/data.frame workflows with grouped operations
  • extract_trends(): Direct time series analysis for ts/xts/zoo objects

• Unified Parameter System: Consistent interface with window, smoothing, band, align, and params parameters across all methods

• Smart Economic Defaults:

  • HP filter: λ=1600 (quarterly), λ=14400 (monthly)
  • Moving averages: Frequency-appropriate windows (4 quarters, 12 months)
  • Bandpass filters: 6-32 quarter business cycle range

• Performance Optimizations:

  • C++ implementations via RcppRoll for fast rolling statistics
  • Optimized exponential smoothing with automatic parameter selection
  • Efficient signal processing filters

Major Improvements

• Mathematical Correctness: All 21 methods validated for theoretical accuracy and proper implementation
• EWMA Dual Interface: Support for both window-based (TTR optimization) and alpha-based (traditional formula) approaches
• One-sided HP Filter: Real-time analysis support with hp_onesided=TRUE parameter for nowcasting and policy analysis
• Align Parameter: Flexible positioning for moving averages (left/center/right) enabling causal and anti-causal filters
• Modern R Patterns: Native pipe |>, cli messaging, comprehensive error handling
• Scale Invariance: Kernel smoother with theoretically sound bandwidth selection
• Robust Error Handling: Informative messages with actionable suggestions using cli package

Quality Metrics

• R CMD check: 0 errors | 0 warnings | 0 notes (perfect score)
• Test suite: 317 passing tests across 9 test files
• Documentation: All examples verified working
• Code quality: No duplicates, modern patterns, clean dependencies

Included Datasets

The package includes 10 economic datasets for examples and testing:

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

Package Scope

Optimized for monthly (frequency=12) and quarterly (frequency=4) economic data, with smart defaults tailored for business cycle analysis. Methods like STL and moving averages also support daily and other frequencies.

Technical Details

• Minimum R version: 4.1.0
• Dependencies: mFilter, hpfilter, RcppRoll, forecast, dlm, signal, tsbox, cli, lubridate, tibble
• License: MIT
• Repository: https://github.com/viniciusoike/trendseries
• Website: https://viniciusoike.github.io/trendseries/

Installation

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

Acknowledgments

This package builds upon excellent work from the R community: mFilter (economic filters), hpfilter (one-sided HP filter), RcppRoll (fast C++ rolling statistics), forecast (exponential smoothing), dlm (Kalman filtering), signal (signal processing), tsbox (time series conversions).