Research map / Time series / Causal and generative time series
Modeling the Intensity Function of Point Process Via Recurrent Neural Networks
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add event-sequence RNN to the time-series RNN (ablation) | ATM failure prediction · ATM testing set | F1 score (main type): 0.707 -> 0.765 | Helped |
| add time-series RNN to the event-sequence RNN (ablation) | ATM failure prediction · ATM testing set | F1 score (main type): 0.703 -> 0.765 | Helped |
| add time-series and event-sequence RNNs together (ablation) | ATM failure prediction · ATM testing set | F1 score (subtype): 0.572 -> 0.616 | Helped |
| use hierarchical rather than flat type prediction (ablation) | Intensity RNN · ATM testing set | F1 score (subtype): 0.616 -> 0.663 | Helped |
| use hierarchical rather than flat type prediction (ablation) | Intensity RNN · ATM testing set | F1 score (main type): 0.765 -> 0.662 | Hurt |
Closest papers by meaning
- EasyTPP: Towards Open Benchmarking Temporal Point Processes (ICLR 2024 · similarity 0.88)
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- Residual TPP: A Unified Lightweight Approach for Event Stream Data Analysis (ICML 2025 · similarity 0.86)
- Decoupled Marked Temporal Point Process using Neural Ordinary Differential Equations (ICLR 2024 · similarity 0.86)
- Deep Continuous-Time State-Space Models for Marked Event Sequences (NeurIPS 2025 · similarity 0.85)
- Neural Jump-Diffusion Temporal Point Processes (ICML 2024 · similarity 0.85)
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