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..
Feature Programming for Multivariate Time Series Prediction
Authors: Alex Daniel Reneau, Jerry Yao-Chieh Hu, Ammar Gilani, Han Liu
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerically, we validate the efficacy of our method on several synthetic and real-world noisy time series datasets. |
| Researcher Affiliation | Academia | 1Department of Computer Science, University of Northwestern, Evanston, USA 2Department of Statistics and Data Science, University of Northwestern, Evanston, USA. |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at Git Hub; the most updated version is available on ar Xiv with a full list of authors, including Chenwei Xu and Weijian Li. We kindly request that citations refer to the ar Xiv version: https://arxiv.org/abs/2306.06252. |
| Open Datasets | Yes | The data utilized in our experiments consists of a synthetic dataset constructed to adhere to the assumptions of our method, as well as an electricity dataset, a traffic dataset, and a taxi dataset. ... Taxi Dataset: We use the TLC Trip Record Dataset... Electricity Dataset: We use the UCI Electricity Load Diagrams Dataset... Traffic Dataset: We use the UCI PEM-SF Traffic Dataset... |
| Dataset Splits | No | Each of these datasets is partitioned in an 80/20 ratio to derive our training data (known as insample data) and testing data (referred to as out-of-sample data). |
| Hardware Specification | Yes | Platforms: The GPUs and CPUs used to conduct experiments are NVIDIA GEFORCE RTX 2080 Ti and INTEL XEON SILVER 4214 @ 2.20GHz. |
| Software Dependencies | No | The paper mentions software like 'DART' and 'XGBoost', 'Light GBM', 'Transformer', 'TFT', 'TCN', 'N-BEATS' but does not specify their version numbers. |
| Experiment Setup | Yes | Hyperparameter optimization is conducted via random search for 100 iterations. learning rate: 0.01, 0.001, 0.0001, 0.00001 batch size: 64, 128, 256, 512, feature dim hidden size: 64, 128, 512, 1024, 2048 num epochs: we use early stopping. ... We use an Adam optimizer with learning rate lr = 10 5 for training. The coefficients of Adam optimizer, betas, are set to (0.9, 0.999). |