---
title: Next-Activity Prediction
type: concept
tags: [ppm, prediction, classification]
sources: []
created: 2026-04-13
updated: 2026-04-13
---

# Next-Activity Prediction

A [[concepts/predictive-process-monitoring|PPM]] task: given a prefix of an event trace, predict the **next activity** the case will execute.

## Formulation
- **Input:** prefix `⟨e₁, e₂, …, eₖ⟩` of a running case.
- **Output:** activity label `eₖ₊₁` ∈ `A` (multi-class classification over activity vocabulary).
- **Training data:** every prefix-continuation pair extracted from completed cases.

## Canonical approaches
- **Probabilistic / Markov** — transition-matrix models over activity sequences.
- **Classical ML** — features extracted via [[concepts/trace-encoding]]; classifier (random forest, decision tree, logistic regression).
- **RNN / [[concepts/lstm-ppm|LSTM]]** — sequence models with activity embeddings; dominant approach 2016–2020.
- **[[concepts/transformer-ppm|Transformer / attention]]** — rising since 2020–2021.
- **Graph Neural Nets (GNN)** — treat the log as a graph; 2023 onward.

## Evaluation
- **Accuracy / top-k accuracy** — did the predicted activity match the true next?
- **F1 (weighted / macro)** — handles class imbalance across activities.
- **Brier score** — if probabilistic outputs are needed.

## Full-suffix variant
Extend the prediction recursively to produce the whole remaining activity sequence. Accumulates error quickly; often evaluated with **Damerau-Levenshtein** distance between predicted and actual suffix.

## Related
[[concepts/predictive-process-monitoring]] · [[concepts/remaining-time-prediction]] · [[concepts/trace-encoding]]