We lifted 10 functions out of this paper's own repositories and ran 6 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 |
|---|---|---|
| aangelopoulos/conformal-time-series | canonical | 6 of 10 |
| Function | Status | Where it lives |
|---|---|---|
| fit_ar_model | Ran | aangelopoulos/conformal-time-series/core/ar.py code served (permissive licence) · get_code("37bbb86d90955318") |
| generate_process | Ran | aangelopoulos/conformal-time-series/core/ar.py code served (permissive licence) · get_code("210e10c5742f02e4") |
| generate_process_heteroskedastic | Ran | aangelopoulos/conformal-time-series/core/ar.py code served (permissive licence) · get_code("37f5e5facb6455c3") |
| generate_scores | Ran | aangelopoulos/conformal-time-series/core/synthetic_scores.py code served (permissive licence) · get_code("bec5ac586d39399b") |
| linear_scores | Ran | aangelopoulos/conformal-time-series/core/synthetic_scores.py code served (permissive licence) · get_code("ba06bf589432338c") |
| sinusoidal_scores | Ran | aangelopoulos/conformal-time-series/core/synthetic_scores.py code served (permissive licence) · get_code("f16fb8fbbed1a83d") |
| aci | Not yet run | aangelopoulos/conformal-time-series/core/methods.py code served (permissive licence) · get_code("541bf2269045ac94") |
| aci_clipped | Not yet run | aangelopoulos/conformal-time-series/core/methods.py code served (permissive licence) · get_code("b1c525a1109057fc") |
| trailing_window | Not yet run | aangelopoulos/conformal-time-series/core/methods.py code served (permissive licence) · get_code("a6b92b9ac05573f0") |
| weighted_conformal | Not yet run | aangelopoulos/conformal-time-series/core/quantile.py code served (permissive licence) · get_code("0d308c17ddd99063") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
We study the problem of uncertainty quantification for time series prediction, with the goal of providing easy-to-use algorithms with formal guarantees. The algorithms we present build upon ideas from conformal prediction and control theory, are able to prospectively model conformal scores in an online setting, and adapt to the presence of systematic errors due to seasonality, trends, and general distribution shifts. Our theory both simplifies and strengthens existing analyses in online conformal prediction. Experiments on 4-week-ahead forecasting of statewide COVID-19 death counts in the U.S. show an improvement in coverage over the ensemble forecaster used in official CDC communications. We also run experiments on predicting electricity demand, market returns, and temperature using autoregressive, Theta, Prophet, and Transformer models. We provide an extendable codebase for testing our methods and for the integration of new algorithms, data sets, and forecasting rules.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2307.16895")
get_code_for_paper("2307.16895")
have("2307.16895")
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