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 Multi-Scale Decomposition Framework for Time Series Forecasting
Authors: Yifan Hu, Peiyuan Liu, Peng Zhu, Dawei Cheng, Tao Dai
AAAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive experiments demonstrate our AMD framework not only overcomes the limitations of existing methods but also consistently achieves state-of-the-art performance across various datasets. |
| Researcher Affiliation | Academia | Yifan Hu1,2,*, Peiyuan Liu1,*, Peng Zhu2, Dawei Cheng2, , Tao Dai3, 1Tsinghua Shenzhen International Graduate School 2Tongji University 3Shenzhen University |
| Pseudocode | No | The paper describes the proposed method in prose and mathematical formulas, but it does not contain a clearly labeled pseudocode or algorithm block. |
| Open Source Code | Yes | Code https://github.com/TROUBADOUR000/AMD |
| Open Datasets | Yes | We conduct experiments on seven real-world datasets, including Weather, ETT (ETTh1, ETTh2, ETTm1, ETTm2), ECL, Exchange, Traffic and Solar Energy for long-term forecasting and PEMS (PEMS03, PEMS04, PEMS07, PEMS08) for short-term forecasting. |
| Dataset Splits | No | The paper specifies input lengths (e.g., "For short-term forecasting, the input length is 96." and "the input sequence length L is searched among {96, 192, 336, 512, 672, 720}") and prediction lengths ("prediction length T is 12") but does not provide explicit training/test/validation dataset splits (e.g., percentages, sample counts, or specific predefined split citations). |
| Hardware Specification | Yes | All experiments are conducted using Py Torch on an NVIDIA V100 32GB GPU and are repeated five times for consistency. |
| Software Dependencies | No | The paper mentions "Py Torch" as a software dependency but does not specify a version number. |
| Experiment Setup | Yes | To ensure fair comparisons, for long-term forecasting, we rerun all baselines with different input lengths L and choose the best results to avoid underestimating the baselines. For short-term forecasting, the input length is 96. We select two common metrics in time series forecasting: Mean Absolute Error (MAE) and Mean Squared Error (MSE). To strike a balance, we set the number of predictors to 8 across all experiments. The total loss function is defined as: L = Lpred + λ1Lselector + λ2 Θ 2 where Θ 2 is the L2-norm, λ1,2 are hyper-parameters. |