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..
Non-stationary Transformers: Exploring the Stationarity in Time Series Forecasting
Authors: Yong Liu, Haixu Wu, Jianmin Wang, Mingsheng Long
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments to evaluate the performance of Non-stationary Transformers on six real-world time series forecasting benchmarks and further validate the generality of the proposed framework on various mainstream Transformer variants. |
| Researcher Affiliation | Academia | Yong Liu , Haixu Wu , Jianmin Wang, Mingsheng Long B School of Software, BNRist, Tsinghua University, China EMAIL, EMAIL |
| Pseudocode | No | The paper describes its methodology using prose and mathematical equations, but it does not include structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at this repository: https://github.com/thuml/Nonstationary_Transformers. |
| Open Datasets | Yes | Datasets Here are the descriptions of the datasets: (1) Electricity [3]... (2) ETT [37]... (3) Exchange [18]... (4) ILI [1]... (5) Traffic [2]... (6) Weather [4]... |
| Dataset Splits | Yes | We follow the standard protocol that divides each dataset into the training, validation, and testing subsets according to the chronological order. The split ratio is 6:2:2 for the ETT dataset and 7:1:2 for others. |
| Hardware Specification | Yes | All the experiments are implemented with PyTorch [28] and conducted on a single NVIDIA TITAN V 12GB GPU. |
| Software Dependencies | No | The paper mentions 'PyTorch [28]' as the implementation framework but does not specify its version number or any other software dependencies with version details. |
| Experiment Setup | Yes | Each model is trained by ADAM [16] using L2 loss with the initial learning rate of 10 4 and batch size of 32. Each Transformer-based model contains two encoder layers and one decoder layer. Considering the efficiency of hyperparameters search, we use two-layer perceptron projector with the hidden dimension varying in {64, 128, 256} in De-stationary Attention. |