We lifted 11 functions out of this paper's own repositories and ran 11 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 |
|---|---|---|
| fionaz3696/cafht | canonical | 11 of 11 |
| Function | Status | Where it lives |
|---|---|---|
| astar_path | Ran | fionaz3696/cafht/media/animation_3d.py pointer only (licence: NONE) · get_code("4e566ca5f4660bc9") |
| astar_path | Ran | fionaz3696/cafht/media/animation_rectangle.py pointer only (licence: NONE) · get_code("e94816ebb49ad1ec") |
| evaluation | Ran | fionaz3696/cafht/ConformalizedTS/evals.py pointer only (licence: NONE) · get_code("af0a444dd2175e9a") |
| evaluation_multivariate | Ran | fionaz3696/cafht/ConformalizedTS/evals.py pointer only (licence: NONE) · get_code("2b190f09aa1d78b4") |
| is_free | Ran | fionaz3696/cafht/media/animations.py pointer only (licence: NONE) · get_code("c65924c0b2b47b3b") |
| mean_step_size | Ran | fionaz3696/cafht/media/animation_rectangle.py pointer only (licence: NONE) · get_code("1193c8f6bee70a19") |
| mean_step_size | Ran | fionaz3696/cafht/media/animations.py pointer only (licence: NONE) · get_code("3550fbcb31aa3c44") |
| mytan | Ran | fionaz3696/cafht/ConformalizedTS/utils.py pointer only (licence: NONE) · get_code("8acfc57a5054c5ef") |
| sliding_window_average | Ran | fionaz3696/cafht/ConformalizedTS/evals.py pointer only (licence: NONE) · get_code("c544cb70b994a6b2") |
| split_train_sequence | Ran | fionaz3696/cafht/ConformalizedTS/utils.py pointer only (licence: NONE) · get_code("c8703d581d0b2c13") |
| trimming | Ran | fionaz3696/cafht/ConformalizedTS/utils.py pointer only (licence: NONE) · get_code("bd6e27bb496e4955") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
This paper presents a new conformal method for generating simultaneous forecasting bands guaranteed to cover the entire path of a new random trajectory with sufficiently high probability. Prompted by the need for dependable uncertainty estimates in motion planning applications where the behavior of diverse objects may be more or less unpredictable, we blend different techniques from online conformal prediction of single and multiple time series, as well as ideas for addressing heteroscedasticity in regression. This solution is both principled, providing precise finite-sample guarantees, and effective, often leading to more informative predictions than prior methods.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2402.09623")
get_code_for_paper("2402.09623")
have("2402.09623")
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