  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.
