  Long-term time-series forecasting (LTTF) has become a pressing demand in many
applications, such as wind power supply planning. Transformer models have been
adopted to deliver high prediction capacity because of the high computational
self-attention mechanism. Though one could lower the complexity of Transformers
by inducing the sparsity in point-wise self-attentions for LTTF, the limited
information utilization prohibits the model from exploring the complex
dependencies comprehensively. To this end, we propose an efficient
Transformerbased model, named Conformer, which differentiates itself from
existing methods for LTTF in three aspects: (i) an encoder-decoder architecture
incorporating a linear complexity without sacrificing information utilization
is proposed on top of sliding-window attention and Stationary and Instant
Recurrent Network (SIRN); (ii) a module derived from the normalizing flow is
devised to further improve the information utilization by inferring the outputs
with the latent variables in SIRN directly; (iii) the inter-series correlation
and temporal dynamics in time-series data are modeled explicitly to fuel the
downstream self-attention mechanism. Extensive experiments on seven real-world
datasets demonstrate that Conformer outperforms the state-of-the-art methods on
LTTF and generates reliable prediction results with uncertainty quantification.
