Research map / Time series / Causal and generative time series
Intensity-Free Learning of Temporal Point Processes
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Model inter-event times with a flexible log-normal mixture instead of a unimodal distribution | Temporal point process event-time prediction · Real-world datasets | test NLL | Helped |
| Use LogNormMix instead of RMTPP/Gompertz | Temporal point process event-time prediction · Real-world datasets | test NLL | Helped |
| Use DSFlow instead of a unimodal distribution | Temporal point process event-time prediction · Real-world datasets | test NLL | Helped |
| Add weekday and evening-hour metadata (ablation) | LogNormMix · Yelp | test loss | Helped |
| Add history embedding (ablation) | LogNormMix · Yelp | test loss | Mixed |
| Fill missing intervals with model-generated samples and optimize expected log-likelihood (ablation) | LogNormMix · Hawkes1, missing-data imputation | NLL on the fully observed sequence | Helped |
| Ignore missing intervals during training (ablation) | LogNormMix · Hawkes1, missing-data imputation | NLL on the fully observed sequence | Hurt |
| Fill missing intervals with the observed-data average inter-event time (ablation) | LogNormMix · Hawkes1, missing-data imputation | NLL on the fully observed sequence | Hurt |
| Use a log-normal mixture rather than a single log-normal distribution | Temporal point process event-time prediction · Real-world datasets | test NLL | Helped |
Closest papers by meaning
- Interacting Diffusion Processes for Event Sequence Forecasting (ICML 2024 · similarity 0.87)
- EasyTPP: Towards Open Benchmarking Temporal Point Processes (ICLR 2024 · similarity 0.87)
- Decomposable Transformer Point Processes (NeurIPS 2024 · similarity 0.86)
- Deep Continuous-Time State-Space Models for Marked Event Sequences (NeurIPS 2025 · similarity 0.86)
- Arbitrarily Conditioned Hierarchical Flows for Spatiotemporal Events (NeurIPS 2026 · similarity 0.85)
- A Variational Autoencoder for Neural Temporal Point Processes with Dynamic Latent Graphs (AAAI 2024 · similarity 0.85)
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