tslagutils: Time Series Lag Utilities

In tspredit: Time Series Prediction with Integrated Tuning

Time Series Lag Utilities

Description

Utility object that groups helper functions used to select lag subsets for sliding-window predictors.

Usage

tslagutils()

Details

These helpers are organized by the type of evidence they use to choose lags.

Positional mappings

• lag_recent() keeps the most recent lags and reproduces the package's original behavior.

• lag_even() spreads the selected lags evenly across the available window.

• lag_geom() emphasizes recent lags while still sampling older history on a geometric scale.

Correlation-driven mappings

• lag_acf() ranks lags by the absolute autocorrelation of the reconstructed training series.

• lag_pacf() ranks lags by the absolute partial autocorrelation.

• lag_peaks() keeps local maxima of the ACF or PACF profile to avoid selecting many redundant neighboring lags.

• lag_seasonal() prioritizes multiples of an estimated or user-provided seasonal period.

• lag_acf_seasonal() and lag_pacf_seasonal() combine seasonal lags with correlation-based completion.

• lag_blocks() expands neighborhoods around the strongest correlation peaks.

Supervised mappings

• lag_mi() ranks lags by discretized mutual information with the target.

• lag_mrmr() greedily maximizes relevance to the target while reducing redundancy among already selected lags.

The mutual-information criteria use quantile discretization and therefore provide deterministic approximations suitable for lightweight dependency-free lag selection inside the package.

Value

A tslagutils object exposing the helper functions.

References

• Box GEP, Jenkins GM, Reinsel GC, Ljung GM (2015). Time Series Analysis: Forecasting and Control. Fifth Edition. Wiley.

• Hyndman RJ, Athanasopoulos G (2021). Forecasting: Principles and Practice. Third Edition. OTexts. https://otexts.com/fpp3/

• Peng H, Long F, Ding C (2005). Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(8), 1226-1238. doi:10.1109/TPAMI.2005.159

• Leites J, Cerqueira V, Soares C (2024). Selecting time lags for time series forecasting: an empirical study. arXiv:2405.11237.

Examples

utils <- tslagutils()

# Positional baselines
utils$lag_recent(total = 9, input_size = 4)
utils$lag_even(total = 9, input_size = 4)

# Reconstruct a raw series from sliding windows and aligned outputs
data(tsd)
ts <- ts_data(tsd$y, 10)
io <- ts_projection(ts)
series <- utils$reconstruct_series(io$input, io$output)
head(series)

# Correlation profile over available lags
utils$score_acf(series, lag_max = 9)

tspredit documentation built on May 15, 2026, 1:07 a.m.