Effectiveness of Recency-Ordered Computation Under Distribution Shifts and Irregular Sampling

Determine whether recency-ordered computation remains effective when the MARO architecture is applied to irregularly sampled time series and to distribution shifts beyond the benchmarks evaluated in the paper.

Background

MARO processes overlapping time-series patches from the most recent observation toward the oldest, using the most recent patch as an anchor and recurrently incorporating older context. The experiments evaluate this recency-ordered computation on regularly sampled benchmark datasets under fixed evaluation settings.

The conclusion identifies irregular sampling and distribution shifts as settings not addressed by the current experiments. The authors propose extending MARO to these settings specifically to test whether the observed benefits of recency ordering generalize beyond the current benchmarks.

References

Future work. Promising directions include parallel or chunked implementations of the recent-to-old scan, input-adaptive sparse channel routing, and broader replicated studies of anchor position and scan order. Extending MARO to irregularly sampled series and distribution shifts would further test whether recency-ordered computation remains effective beyond the current benchmarks.

— Most-Recent Anchoring with Recurrent Ordering for Time Series Forecasting  (2610.03494 - Choi et al., 2 Oct 2026) in Section 5, Conclusion, Future work (page 10)