Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Adaptive Conformal Predictions for Time Series
Authors: Margaux Zaffran, Olivier Feron, Yannig Goude, Julie Josse, Aymeric Dieuleveut
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We lead extensive fair simulations against competing methods that advocate for ACI s use in time series. We conduct a real case study: electricity price forecasting. |
| Researcher Affiliation | Collaboration | 1Electricit e de France R&D, Palaiseau, France 2INRIA Sophia Antipolis, Montpellier, France 3CMAP, Ecole Polytechnique, Institut Polytechnique de Paris, Palaiseau, France 4Fi ME, Universit e Paris-Dauphine, France 5LMO, Universit e Paris-Saclay, Orsay, France 6IDESP, Montpellier, France. |
| Pseudocode | Yes | Algorithm 1 Online Expert Aggregation on ACI (Ag ACI) |
| Open Source Code | Yes | All the code and data to reproduce the experiments are made available on Git Hub. |
| Open Datasets | No | The data set contains the French electricity spot prices, set by an auction market, from 2016 to 2019. |
| Dataset Splits | Yes | We predict for the year 2019, using a sliding window of 3 years, as described in Figure 5(a), using one year and 6 months as proper training set and the most recent year and a half for calibration. |
| Hardware Specification | No | The paper does not mention any specific hardware details such as CPU, GPU models, or memory specifications used for the experiments. |
| Software Dependencies | No | To allow for better benchmarking of existing and new methods, we provide (re-)implementations in Python of (all) the described methods and a complete pipeline of analysis on Git Hub. As explained in Section 4, the code for Ag ACI is, for now, the only one available only in R. |
| Experiment Setup | Yes | All the random forests model have the same parameters, that are the following: Number of trees: 1000 Minimum sample per leaf: 1 (default) Maximum number of features: d (default) |