The evolution of statistical process monitoring

From SPC control charts to machine-learning process monitoring — what changed, and what it means for quality teams.

LSTM autoencoder for anomaly detection in Keras

A widely cited walkthrough of building an LSTM autoencoder for rare-event detection in multivariate time series.

Building autoencoders in Keras

Companion tutorial covering autoencoder fundamentals and implementation for dimensionality reduction and reconstruction.

A rare-event dataset from a pulp-and-paper process (Part 1)

The multivariate time-series dataset behind the rare-event series — widely referenced across the ML community.

Rare-event classification in multivariate time series (Part 2)

Modeling approaches for extreme class imbalance, applied to a rare-event downtime dataset.

Sequence embedding for clustering and classification

Turning variable-length sequences into fixed embeddings for downstream clustering and classification tasks.

Estimating non-linear correlation in R

A practical method for quantifying non-linear relationships between process variables.