Yunyeong Kim  Sungwon jung

10.5626/JOK.2024.51.7.663

Time Series Forecasting GRU Patching Transformer

Time series forecasting plays a crucial role in decision-making within various fields. Two recent approaches, namely, the patch time series Transformer (PatchTST) and the long-term time series foraging linear (LTSF-Linear) of the MLP structure have shown promising performance in this area. However, PatchTST requires significant time for both model training and inference, while LTSF-Linear has limited capacity due to its simplistic structure. To address these limitations, we propose a new approach called patch time series GRU (PatchTSG). By leveraging a Gated Recurrent Unit (GRU) on the patched data, PatchTSG reduces the training time and captures valuable information from the time series data. Compared to PatchTST, PatchTSG achieves an impressive reduction in learning time (up to 82%) and inference time (up to 46%).