xLSTMTime : Long-term Time Series Forecasting With xLSTM
<|reference_start|>xLSTMTime : Long-term Time Series Forecasting With xLSTM: 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.<|reference_end|>
@article{alharthi2024xlstmtime,
    title={xLSTMTime : Long-term Time Series Forecasting With xLSTM},
    author={Musleh Alharthi and Ausif Mahmood},
    journal={arXiv preprint arXiv:2407.10240},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.10240},
    primaryClass={cs.LG cs.AI}
}