Adaptive Conformal Predictions for Time Series
Authors: Margaux Zaffran, Olivier Feron, Yannig Goude, Julie Josse, Aymeric Dieuleveut
ICML 2022 | Conference PDF | Archive PDF | Plain Text | 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) |