Kurt Butler, Daniel Waxman, Petar Djurić
We lifted 8 functions out of this paper's own repositories and ran 0 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| KurtButler/tangentspaces | canonical | 0 of 8 |
| Function | Status | Where it lives |
|---|---|---|
| auto_window | Not yet run | KurtButler/tangentspaces/python/utils.py code served (permissive licence) · get_code("b9a951d367c8d9ff") |
| autocorr_func_1d | Not yet run | KurtButler/tangentspaces/python/utils.py code served (permissive licence) · get_code("2258cab64f4fe6dd") |
| compress_df | Not yet run | KurtButler/tangentspaces/python/latent_tsci/experiments/Dpendulum/gruode_scores.py code served (permissive licence) · get_code("d7e31697a7ff00fe") |
| embed_time_series | Not yet run | KurtButler/tangentspaces/python/latent_tsci/experiments/Dpendulum/gruode_scores.py code served (permissive licence) · get_code("5ac066a67be06009") |
| embed_time_series | Not yet run | KurtButler/tangentspaces/python/latent_tsci/latentccm/causal_inf.py code served (permissive licence) · get_code("7e8f863a84ebac0f") |
| estimate_threshold | Not yet run | KurtButler/tangentspaces/python/rossler_lorenz_sine_wave.py code served (permissive licence) · get_code("7213a7c9a6067de0") |
| next_pow_two | Not yet run | KurtButler/tangentspaces/python/utils.py code served (permissive licence) · get_code("a87ef11e3dd91c9f") |
| tsci_torch | Not yet run | KurtButler/tangentspaces/python/tsci.py code served (permissive licence) · get_code("893db20c8e8c8dbd") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Causal discovery with time series data remains a challenging yet increasingly important task across many scientific domains. Convergent cross mapping (CCM) and related methods have been proposed to study time series that are generated by dynamical systems, where traditional approaches like Granger causality are unreliable. However, CCM often yields inaccurate results depending upon the quality of the data. We propose the Tangent Space Causal Inference (TSCI) method for detecting causalities in dynamical systems. TSCI works by considering vector fields as explicit representations of the systems' dynamics and checks for the degree of synchronization between the learned vector fields. The TSCI approach is modelagnostic and can be used as a drop-in replacement for CCM and its generalizations. We first present a basic version of the TSCI algorithm, which is shown to be more effective than the basic CCM algorithm with very little additional computation. We additionally present augmented versions of TSCI that leverage the expressive power of latent variable models and deep learning. We validate our theory on standard systems, and we demonstrate improved causal inference performance across a number of benchmark tasks.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2410.23499")
get_code_for_paper("2410.23499")
have("2410.23499")
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