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A General Spatio-Temporal Backbone with Scalable Contextual Pattern Bank for Urban Continual Forecasting

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
general backbonecontextual pattern bankcontinual spatio-temporal forecasting
Abstract:

With the rapid growth of spatio-temporal data fueled by IoT deployments and urban infrastructure expansion, accurate and efficient continual forecasting has become a critical challenge. Most existing Spatio-Temporal Graph Neural Networks rely on static graph structures and offline training, rendering them inadequate for real-world streaming scenarios characterized by node expansion and distribution shifts. Although Continual Spatio-Temporal Forecasting methods have been proposed to tackle these issues, they often adopt backbones with limited modeling capacity and lack effective mechanisms to balance stability and adaptability. To overcome these limitations, we propose STBP, a novel framework that integrates a general spatio-temporal backbone with a scalable contextual pattern bank. The backbone extracts stable representations in the frequency domain and captures dynamic spatial correlations through lightweight linear graph attention. To support continual adaptation and mitigate catastrophic forgetting, the contextual pattern bank is updated incrementally via parameter expansion, enabling the capture of evolving node-level heterogeneity and relevance. During incremental training, the backbone remains fixed to preserve general knowledge, while the pattern bank adapts to new scenarios and distributions. Extensive experiments demonstrate that STBP outperforms state-of-the-art baselines in both forecasting accuracy and scalability, validating its effectiveness for continual spatio-temporal forecasting.

Disclaimer
This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper proposes STBP, a framework combining a general spatio-temporal backbone with a scalable contextual pattern bank for continual forecasting. It resides in the 'Replay-Based and Memory-Augmented Continual Learning' leaf, which contains only three papers including this one. This leaf sits within the broader 'Continual Learning Mechanisms' branch, indicating a moderately populated research direction focused on mitigating catastrophic forgetting through memory structures. The small sibling count suggests this specific combination of backbone design and pattern bank mechanisms occupies a relatively sparse niche within continual spatio-temporal forecasting.

The taxonomy reveals neighboring approaches across five sibling leaves: parameter expansion methods that grow model capacity, distribution-aware techniques handling test-time shifts, meta-learning frameworks enabling rapid adaptation, and structural knowledge preservation methods. The paper's leaf explicitly excludes fixed-capacity models and parameter-based approaches, positioning it among methods that retain past knowledge through explicit memory rather than architectural growth alone. This boundary distinguishes STBP from modular expansion strategies while aligning it with replay-buffer traditions, though the contextual pattern bank appears to blend memory augmentation with incremental parameter updates.

Among twenty-eight candidates examined across three contributions, none yielded clear refutations. The general backbone contribution examined ten candidates with zero refutable overlaps; the pattern bank mechanism similarly found no prior work among ten candidates; the integrated framework analyzed eight candidates without refutation. This absence of overlapping prior work within the limited search scope suggests the specific combination of frequency-domain representations, lightweight graph attention, and prompt-guided pattern banks may be relatively unexplored. However, the modest search scale means potentially relevant work outside the top-K semantic matches remains unexamined, limiting definitive novelty claims.

Based on the examined candidates, the framework appears to occupy a distinct position blending memory-augmented continual learning with advanced spatio-temporal modeling. The lack of refutable prior work across all contributions within the search scope indicates potential novelty, though the analysis covers only a targeted subset of the literature. The sparse population of the taxonomy leaf and absence of overlapping mechanisms among examined papers suggest the work may introduce fresh combinations, pending broader literature coverage.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
28
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: continual spatio-temporal forecasting addresses the challenge of maintaining predictive accuracy as spatial and temporal patterns evolve over time, requiring models to adapt without catastrophic forgetting. The field's taxonomy reveals several complementary directions: Continual Learning Mechanisms focus on strategies like replay buffers and memory augmentation to preserve past knowledge while integrating new data; Graph-Based Architectures such as Continual Graph Convolutional[1] leverage relational structures to capture spatial dependencies; Attention-Based methods including Continuous Spatiotemporal Transformers[2] and Multi-Scale Transformer[8] emphasize dynamic feature weighting across space and time; Recurrent and Sequential Models like PredRNN[7] handle temporal dependencies through hidden states; Convolutional and Multi-View approaches such as Deep Multi-View[4] extract hierarchical patterns; Generative and Probabilistic methods model uncertainty; Domain-Specific Applications target areas like traffic, climate, and energy forecasting; and Specialized Techniques provide auxiliary tools for robustness and efficiency. A particularly active line of work explores how to balance stability and plasticity in continual settings, with some studies employing replay mechanisms to mitigate forgetting while others investigate architectural expansions or compression strategies. The Spatio-Temporal Backbone[0] sits within the Replay-Based and Memory-Augmented Continual Learning branch, closely aligned with works like Unified Replay Framework[5] and Expand and Compress[6], which similarly address catastrophic forgetting through memory management and dynamic model capacity. While Unified Replay Framework[5] emphasizes systematic replay strategies and Expand and Compress[6] focuses on adaptive architecture scaling, Spatio-Temporal Backbone[0] appears to integrate these themes within a foundational framework for continual adaptation. This cluster contrasts with purely graph-centric methods like Continual Graph Convolutional[1] or attention-driven approaches such as Continuous Spatiotemporal Transformers[2], highlighting ongoing debates about whether continual learning is best achieved through explicit memory mechanisms, architectural flexibility, or hybrid strategies that combine spatial-temporal inductive biases with adaptive learning protocols.

Claimed Contributions

General spatio-temporal backbone for continual forecasting

The authors introduce a general-purpose spatio-temporal backbone that uses frequency-domain networks to extract stable temporal components and a dual-stream linear graph attention mechanism to capture dynamic spatial correlations. This backbone is designed to handle distributional drift and scale to evolving graph structures without relying on predefined adjacency matrices.

10 retrieved papers
Scalable contextual pattern bank with prompt-based guidance

The authors propose a contextual pattern bank composed of trainable parameters that incrementally expands via parameter expansion to capture node-level heterogeneity and relevance. This bank interacts with the frozen backbone through gating and attention mechanisms, enabling continual adaptation to new scenarios while preserving historical knowledge without requiring access to past data.

10 retrieved papers
STBP framework integrating backbone and pattern bank

The authors present STBP, a unified framework where the backbone remains fixed after initial training to preserve general knowledge, while the contextual pattern bank adapts through incremental updates. This collaborative design balances stability and adaptability for continual spatio-temporal forecasting in streaming urban data scenarios.

8 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

General spatio-temporal backbone for continual forecasting

The authors introduce a general-purpose spatio-temporal backbone that uses frequency-domain networks to extract stable temporal components and a dual-stream linear graph attention mechanism to capture dynamic spatial correlations. This backbone is designed to handle distributional drift and scale to evolving graph structures without relying on predefined adjacency matrices.

Contribution

Scalable contextual pattern bank with prompt-based guidance

The authors propose a contextual pattern bank composed of trainable parameters that incrementally expands via parameter expansion to capture node-level heterogeneity and relevance. This bank interacts with the frozen backbone through gating and attention mechanisms, enabling continual adaptation to new scenarios while preserving historical knowledge without requiring access to past data.

Contribution

STBP framework integrating backbone and pattern bank

The authors present STBP, a unified framework where the backbone remains fixed after initial training to preserve general knowledge, while the contextual pattern bank adapts through incremental updates. This collaborative design balances stability and adaptability for continual spatio-temporal forecasting in streaming urban data scenarios.

Table of Contents