Revisiting Recurrent Models for Long-Term Time Series Forecasting through Representation Accessibility

Transformer-based architectures have become prominent in long-term time series forecasting (LTSF), and their empirical advantages are often attributed to self-attention. This paper revisits that attribution by identifying a less visible architectural confounder in common LTSF comparisons: representation accessibility. In many direct multi-horizon recurrent baselines, the prediction module is conditioned on a compressed recurrent summary, typically the final encoder state, or on a decoder initialized from that state. In contrast, patch-based Transformer forecasters such as PatchTST expose representations from multiple temporal positions directly to a non-autoregressive prediction head. This difference changes not only the sequence modeling mechanism, but also which historical representations are accessible at prediction time. To isolate this factor, we introduce a Full-State Readout (FSR) framework built from standard recurrent encoders. FSR keeps key ingredients of strong LTSF forecasters fixed, including patching, channel independence, normalization, and a Flatten–Linear prediction head, while varying only the number of recurrent hidden states exposed to the head. Across six LTSF benchmarks, FSR with a GRU backbone matches or outperforms representative Transformer baselines. Expanding representation accessibility generally reduces forecasting errors for GRU, and full-state readout also improves vanilla RNN and LSTM encoders. These results show that the recurrent–Transformer gap in LTSF cannot be attributed solely to recurrence versus self-attention. More broadly, they suggest that final-state bottlenecks can systematically underestimate recurrent models, and that representation accessibility should be controlled when comparing sequence modeling architectures for forecasting.