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-aligned N-BEATS with Sinkhorn divergence
Authors: Joonhun Lee, Myeongho Jeon, Myungjoo Kang, Kyunghyun Park
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive experimental evaluations with ablation studies are provided and the corresponding results demonstrate the proposed model s forecasting and generalization capabilities. |
| Researcher Affiliation | Academia | Joonhun Lee , Myeongho Jeon & Myungjoo Kang Seoul National University EMAIL Kyunghyun Park Nanyang Technological University EMAIL |
| Pseudocode | Yes | Algorithm 1: Training Feature-aligned N-BEATS. |
| Open Source Code | Yes | The comprehensive software setup can be found on Git Hub3. (footnote 3: https://github.com/leejoonhun/fan-beats) |
| Open Datasets | Yes | For (i), we use financial data from the Federal Reserve Economic Data (FRED)1 and weather data from the National Centers for Environmental Information (NCEI)2. (footnote 1: https://fred.stlouisfed.org, footnote 2: https://ncei.noaa.gov) ... 4. We randomly split each domain into three sets: 70% for training, 10% for validation, and 20% for testing. |
| Dataset Splits | Yes | 4. We randomly split each domain into three sets: 70% for training, 10% for validation, and 20% for testing. |
| Hardware Specification | Yes | CPU: Intel(R) Xeon(R) Platinum 8163 CPU @ 2.50GHz. GPU: NVIDIA TITAN RTX. |
| Software Dependencies | No | The paper mentions using |
| Experiment Setup | Yes | Hyperparameter Considered Values Lookback horizon. α = 50 Forecast horizon. β = 10 Number of stacks. M = 3 Number of blocks in each stack. L = 4 Activation function. RELU Feature dimension. γ = 512 Loss function. L = s MAPE Regularizing temperature. λ {0.1, 0.3, 1, 3} Learning rate scheduling. Cyclic LR(base lr=2e-7, max lr=2e-5, mode= |