Are Transformers Effective for Time Series Forecasting?

Authors: Ailing Zeng, Muxi Chen, Lei Zhang, Qiang Xu

AAAI 2023 | Conference PDF | Archive PDF | Plain Text | 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 {zengailing, leizhang}@idea.edu.cn,{mxchen21, qxu}@cse.cuhk.edu.hk
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}.