Most-Recent Anchoring with Recurrent Ordering for Time Series Forecasting

2026-10-02

AI Summary

This study addresses the limitation of existing long-horizon forecasting models that employ fixed stacking, which applies uniform computational depth to both recent and distant evidence, thereby failing to effectively distinguish information recency. To overcome this, we propose MARO, a model that recursively scans from the most recent to the oldest data chunk, initializing and conditioning subsequent steps with the latest observation as an anchor to fold historical context into a recency-centric representation. By leveraging a shared recurrent module, this architecture extends the scanning range without increasing parameter count, utilizing intermediate states to achieve differentiated weighting for short- and long-term history. Experiments on multiple real-world datasets demonstrate state-of-the-art performance in both short- and long-term forecasting, while ablation studies validate the critical contributions of recursive ordering, recency anchoring, and parameter sharing mechanisms.

Abstract

Long-term forecasting models commonly process all patches in a look-back window using the same fixed stack. Older contextual patches and recent evidence therefore receive the same computational depth. Yet the information closest to the forecast and the more distant context do not contribute equally. Uniform processing leaves this distinction unexpressed in the architecture. We propose MARO, a Most-Recent Anchoring with Recurrent Ordering model that processes the look-back window from the most recent patch to the oldest. The most recent patch serves as the anchor. It initializes the latent state and conditions each subsequent step, so older patches are folded into a representation that remains centered on recent evidence. A single shared module is reused at every step, so extending the scan further into the past introduces no additional parameters. Intermediate states retained during the scan allow the forecast head to weigh short and long portions of the history separately. This expresses recency through the order of recurrent refinement. Extensive experiments across multiple real-world time series datasets show that MARO achieves state-of-the-art performance on both long-term and short-term forecasting tasks.Ablation studies examine the contribution of the main architectural components.

Problem

• Time Series Forecasting
• Long-term Forecasting
• Recency Bias
• Look-back Window
• Patch Processing

Innovation

• Time Series Forecasting
• Most-Recent Anchoring
• Recurrent Ordering
• Parameter Sharing
• Recency Bias