Research map / Time series / Causal and generative time series
Infinite Hidden Semi-Markov Modulated Interaction Point Process
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Unified observation and arrival-time modelling (iHSMM-IPP) vs. interaction-only and state-only baselines | iHSMM-IPP · Synthetic, REDD, Pipe | Hamming distance error / MSE | Helped |
| Joint modelling of state and triggering kernels vs. marked Hawkes baselines (M-MHawkes, VI-MHawkes) | iHSMM-IPP · Synthetic | Diff distance: 0.36 vs 0.55 (M-MHawkes), 0.62 (VI-MHawkes) | Helped |
| Unified model vs. HSMM and sticky HDP-HMM baselines (state estimation) | iHSMM-IPP · REDD | Hamming distance error: 0.39 vs 0.52 (HDP-HSMM), 0.59 (S-HDP-HMM) | Helped |
| Unified model vs. baselines (pipe failure prediction) | iHSMM-IPP · Pipe | MSE Failures and MSE Hours: 82.8 vs 103.8 (HDP-HSMM) for failures; 28.6 vs 42.3 for hours | Helped |
Closest papers by meaning
- Interaction Point Processes via Infinite Branching Model (AAAI 2016 · similarity 0.88)
- Forward-Backward Latent State Inference for Hidden Continuous-Time semi-Markov Chains (NeurIPS 2022 · similarity 0.88)
- Nonparametric Regressive Point Processes Based on Conditional Gaussian Processes (NeurIPS 2019 · similarity 0.88)
- Mutually Regressive Point Processes (NeurIPS 2019 · similarity 0.86)
- Deep Continuous-Time State-Space Models for Marked Event Sequences (NeurIPS 2025 · similarity 0.85)
- Interacting Diffusion Processes for Event Sequence Forecasting (ICML 2024 · similarity 0.84)
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