# History repeats Itself: A Baseline for Temporal Knowledge Graph Forecasting

arXiv: https://arxiv.org/abs/2404.16726 · HTML: https://mlsanity.com/paper/2404.16726/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 31 references that are in the map.
- First posted on arXiv 25 Apr 2024.

## Builds on (most cited first, 14 of 14)

- [Pitfalls of Graph Neural Network Evaluation](https://mlsanity.com/paper/1811.05868/) (2018)
- [A Fair Comparison of Graph Neural Networks for Graph Classification](https://mlsanity.com/paper/1912.09893/) (2019)
- [Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches](https://mlsanity.com/paper/1907.06902/) (2019)
- [Temporal Graph Benchmark for Machine Learning on Temporal Graphs](https://mlsanity.com/paper/2307.01026/) (2023)
- [Towards Better Evaluation for Dynamic Link Prediction](https://mlsanity.com/paper/2207.10128/) (2022)
- [Temporal Knowledge Graph Reasoning Based on Evolutional Representation Learning](https://mlsanity.com/paper/2104.10353/) (2021)
- [Learning from History: Modeling Temporal Knowledge Graphs with Sequential Copy-Generation Networks](https://mlsanity.com/paper/2012.08492/) (2020)
- [A Re-evaluation of Knowledge Graph Completion Methods](https://mlsanity.com/paper/1911.03903/) (2019)
- [TimeTraveler: Reinforcement Learning for Temporal Knowledge Graph Forecasting](https://mlsanity.com/paper/2109.04101/) (2021)
- [Temporal Knowledge Graph Forecasting with Neural ODE](https://mlsanity.com/paper/2101.05151/) (2021)
- [TLogic: Temporal Logical Rules for Explainable Link Forecasting on Temporal Knowledge Graphs](https://mlsanity.com/paper/2112.08025/) (2021)
- [Temporal Knowledge Graph Reasoning with Historical Contrastive Learning](https://mlsanity.com/paper/2211.10904/) (2022)
- [Search from History and Reason for Future: Two-stage Reasoning on Temporal Knowledge Graphs](https://mlsanity.com/paper/2106.00327/) (2021)
- [Complex Evolutional Pattern Learning for Temporal Knowledge Graph Reasoning](https://mlsanity.com/paper/2203.07782/) (2022)

## Built on by (most cited first, 15 of 15)

- [Position: Graph Learning Will Lose Relevance Due To Poor Benchmarks](https://mlsanity.com/paper/2502.14546/) (2025)
- [TGB 2.0: A Benchmark for Learning on Temporal Knowledge Graphs and Heterogeneous Graphs](https://mlsanity.com/paper/2406.09639/) (2024)
- [A Multi-Expert Structural-Semantic Hybrid Framework for Unveiling Historical Patterns in Temporal Knowledge Graphs](https://mlsanity.com/paper/2506.14235/) (2025)
- [Toward Better Temporal Structures for Geopolitical Events Forecasting](https://mlsanity.com/paper/2601.00430/) (2026)
- [CountTRuCoLa: Rule Learning for Interpretable Temporal Knowledge Graph Forecasting](https://mlsanity.com/paper/2509.09474/) (2025)
- [Beyond Known Facts: Generating Unseen Temporal Knowledge to Address Data Contamination in LLM Evaluation](https://mlsanity.com/paper/2601.13658/) (2026)
- [Strikingness-Aware Evaluation for Temporal Knowledge Graph Reasoning](https://mlsanity.com/paper/2605.13153/) (2026)
- [Temporal Knowledge Graph Forecasting under Distribution Shifts: A Synthetic Evaluation](https://mlsanity.com/paper/2607.09232/) (2026)
- [FITTER: Vocabulary-Agnostic Cross-Domain Inference on Temporal Knowledge Graphs](https://mlsanity.com/paper/2608.10668/) (2026)
- [EdgeReMIND: A Scalable, Top-Ranked Memorization Baseline for Temporal Multi-Relational Link Prediction](https://mlsanity.com/paper/2609.17916/) (2026)
- [Time-Aligned Evolving Concept Graphs for Scientific Relation Forecasting](https://mlsanity.com/paper/2609.18163/) (2026)
- [SCoR: A Hierarchical Framework for Forecasting Relations Between Scientific Concepts](https://mlsanity.com/paper/2609.22174/) (2026)
- [A Temporal Knowledge Graph for Music Festival Lineup Forecasting](https://mlsanity.com/paper/2609.24467/) (2026)
- [BridgeMem: Causal Dyadic Transition Residuals for Temporal Knowledge Graph Forecasting](https://mlsanity.com/paper/2609.29268/) (2026)
- [Learning Across Temporal Graph Representation Silos](https://mlsanity.com/paper/2610.19576/) (2026)

## Abstract (arXiv)

Temporal Knowledge Graph (TKG) Forecasting aims at predicting links in Knowledge Graphs for future timesteps based on a history of Knowledge Graphs. To this day, standardized evaluation protocols and rigorous comparison across TKG models are available, but the importance of simple baselines is often neglected in the evaluation, which prevents researchers from discerning actual and fictitious progress. We propose to close this gap by designing an intuitive baseline for TKG Forecasting based on predicting recurring facts. Compared to most TKG models, it requires little hyperparameter tuning and no iterative training. Further, it can help to identify failure modes in existing approaches. The empirical findings are quite unexpected: compared to 11 methods on five datasets, our baseline ranks first or third in three of them, painting a radically different picture of the predictive quality of the state of the art.