hcp_waypoint: Waypoint: adaptive change-point detection with autoencoder...

In harbinger: A Unified Time Series Event Detection Framework

Waypoint: adaptive change-point detection with autoencoder and CUSUM

Description

Implements the adaptive change-point detector described in the associated SBBD article: a non-supervised autoencoder learns a reference regime from temporal windows, the reconstruction error is standardized on a recent buffer, and a bilateral CUSUM supervisor validates persistent deviations. When a change is confirmed, the model is retrained on recent data and the supervisor is reset.

Usage

hcp_waypoint(
  input_size,
  encode_size,
  warmup = 500,
  retrain_size = 300,
  buffer_size = 100,
  k_factor = 0.35,
  h_low = 3.5,
  h_high = 6,
  prob_tau = 0.997,
  epochs_init = 40,
  epochs_retrain = 20,
  encoderclass = autoenc_base_ed,
  ...
)

Arguments

input_size	Integer. Window size used to build autoencoder samples.
encode_size	Integer. Latent size for the autoencoder.
warmup	Integer. Number of leading observations used to initialize the model.
retrain_size	Integer. Number of recent observations used when retraining after a confirmed change.
buffer_size	Integer. Number of previous residuals used to standardize the current reconstruction error.
k_factor	Numeric. CUSUM reference value (k).
h_low	Numeric. Warning threshold for the bilateral CUSUM supervisor.
h_high	Numeric. Confirmation threshold for the bilateral CUSUM supervisor.
prob_tau	Numeric. Quantile used to estimate the residual threshold from the warm-up regime.
epochs_init	Integer. Epochs for the initial autoencoder training.
epochs_retrain	Integer. Epochs for retraining after a confirmed change.
encoderclass	DALToolbox autoencoder constructor. Defaults to autoenc_base_ed.
...	Additional arguments forwarded to encoderclass; these are the autoencoder-specific parameters, not hcp_waypoint parameters.

Details

The method separates representation and decision:

• the autoencoder reconstructs windows and produces a scalar reconstruction error;

• the error is standardized with a rolling buffer;

• a bilateral CUSUM with lower and upper thresholds (h_low, h_high) acts as the statistical supervisor;

• after confirmation, the autoencoder is retrained on the most recent regime.

This detector is intended for regime-change monitoring rather than isolated anomaly marking.

Value

hcp_waypoint object.

References

• Ogasawara, E., Salles, R., Porto, F., Pacitti, E. (2025). Event Detection in Time Series.

• Salles, R. et al. (2020). Harbinger: Um framework para integração e análise de métodos de detecção de eventos em séries temporais. SBBD.

• Ygorra, B. et al. (2021). Monitoring loss of tropical forest cover from Sentinel-1 time-series: A CuSum-based approach.

• Ygorra, B. et al. (2024). A near-real-time tropical deforestation monitoring algorithm based on the CuSum change detection method.

• De Ryck, T. et al. (2021). Change Point Detection in Time Series Data Using Autoencoders with a Time-Invariant Representation.

• Cao, Z. et al. (2024). Change Point Detection in Multi-Channel Time Series via a Time-Invariant Representation.

• Corizzo, R. et al. (2022). CPDGA: Change point driven growing auto-encoder for lifelong anomaly detection.

Examples

library(daltoolbox)

data(examples_changepoints)
dataset <- examples_changepoints$simple

model <- hcp_waypoint(input_size = 12, encode_size = 4)
model <- fit(model, dataset$serie)
detection <- detect(model, dataset$serie)
print(detection[detection$event, ])

harbinger documentation built on May 8, 2026, 5:07 p.m.