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..
Are Transformers Effective for Time Series Forecasting?
Authors: Ailing Zeng, Muxi Chen, Lei Zhang, Qiang Xu
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on nine real-life datasets show that LTSF-Linear surprisingly outperforms existing sophisticated Transformer-based LTSF models in all cases, and often by a large margin. |
| Researcher Affiliation | Collaboration | Ailing Zeng1,2*, Muxi Chen1*, Lei Zhang2, Qiang Xu1 1The Chinese University of Hong Kong 2International Digital Economy Academy EMAIL,EMAIL |
| Pseudocode | No | The paper does not contain any formally labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not include any explicit statement or link indicating the release of open-source code for the methodology described. |
| Open Datasets | Yes | We conduct extensive experiments on nine widely-used real-world datasets, including ETT (Electricity Transformer Temperature) (Zhou et al. 2021) (ETTh1, ETTh2, ETTm1, ETTm2), Traffic, Electricity, Weather, ILI, Exchange-Rate (Lai et al. 2017). |
| Dataset Splits | No | The paper mentions using training and testing data but does not explicitly provide details about validation dataset splits (e.g., percentages or counts). |
| Hardware Specification | Yes | it is unclear whether 1) the actual inference time and memory cost on devices are improved, and 2) the memory issue is unacceptable and urgent for today s GPU (e.g., an NVIDIA Titan XP here). |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies or libraries used in the experiments. |
| Experiment Setup | Yes | We conduct extensive experiments on nine widely-used real-world datasets, including ETT (Electricity Transformer Temperature) (Zhou et al. 2021) (ETTh1, ETTh2, ETTm1, ETTm2), Traffic, Electricity, Weather, ILI, Exchange-Rate (Lai et al. 2017). ... The MSE results (Y-axis) of models with different look-back window sizes (X-axis) of long-term forecasting (T=720) on Electricity. ... the forecasting lengths {96, 192, 336, 720}. |