Timesnet: Temporal 2d-variation modeling for general time series analysis H Wu, T Hu, Y Liu, H Zhou, J Wang, M Long The eleventh international conference on learning representations, 2022 | 284 | 2022 |
Non-stationary transformers: Exploring the stationarity in time series forecasting Y Liu, H Wu, J Wang, M Long Advances in Neural Information Processing Systems 35, 9881-9893, 2022 | 208 | 2022 |
Logme: Practical assessment of pre-trained models for transfer learning K You, Y Liu, J Wang, M Long International Conference on Machine Learning, 12133-12143, 2021 | 145 | 2021 |
itransformer: Inverted transformers are effective for time series forecasting Y Liu, T Hu, H Zhang, H Wu, S Wang, L Ma, M Long arXiv preprint arXiv:2310.06625, 2023 | 81 | 2023 |
Koopa: Learning non-stationary time series dynamics with koopman predictors Y Liu, C Li, J Wang, M Long Advances in Neural Information Processing Systems 36, 2024 | 21 | 2024 |
Ranking and tuning pre-trained models: a new paradigm for exploiting model hubs K You, Y Liu, Z Zhang, J Wang, MI Jordan, M Long Journal of Machine Learning Research 23 (209), 1-47, 2022 | 21 | 2022 |
AutoTimes: Autoregressive Time Series Forecasters via Large Language Models Y Liu, G Qin, X Huang, J Wang, M Long arXiv preprint arXiv:2402.02370, 2024 | 5 | 2024 |
TimeXer: Empowering Transformers for Time Series Forecasting with Exogenous Variables Y Wang, H Wu, J Dong, Y Liu, Y Qiu, H Zhang, J Wang, M Long arXiv preprint arXiv:2402.19072, 2024 | 2 | 2024 |
Timer: Transformers for Time Series Analysis at Scale Y Liu, H Zhang, C Li, X Huang, J Wang, M Long arXiv preprint arXiv:2402.02368, 2024 | 1 | 2024 |