xLSTMTime

paper: https://arxiv.org/pdf/2407.10240

xLSTMTime for time series forecasting

Abstract: In recent years, transformer-based models have gained prominence in multivariate long-term time series forecasting (LTSF), demonstrating significant advancements despite facing challenges such as high computational demands, difficulty in capturing temporal dynamics, and managing long-term dependencies. The emergence of LTSF-Linear, with its straightforward linear architecture, has notably outperformed transformer-based counterparts, prompting a reevaluation of the transformer's utility in time series forecasting. In response, this paper presents an adaptation of a recent architecture termed extended LSTM (xLSTM) for LTSF. xLSTM incorporates exponential gating and a revised memory structure with higher capacity that has good potential for LTSF. Our adopted architecture for LTSF termed as xLSTMTime surpasses current approaches. We compare xLSTMTime's performance against various state-of-the-art models across multiple real-world da-tasets, demonstrating superior forecasting capabilities. Our findings suggest that refined recurrent architectures can offer competitive alternatives to transformer-based models in LTSF tasks, po-tentially redefining the landscape of time series forecasting.

datasets link https://drive.google.com/drive/mobile/folders/1ZOYpTUa82_jCcxIdTmyr0LXQfvaM9vIy

Update

The original xLSTMTime repository (https://github.com/muslehal/xLSTMTime) had numerous issues. To make it easier for everyone to use, I have modified the source code, and it is now running smoothly. I hope you find it helpful! If you encounter any further issues, please feel free to report them in the issues section!

补充

原版的xLSTMTime仓库源码(https://github.com/muslehal/xLSTMTime) 存在大量问题，为了方便大家使用，我修改了源码，目前代码已经能够顺利跑通，希望大家使用愉快！若还有问题，欢迎大家在issues中提出！