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Tackling Time-Series Forecasting Generalization via Mitigating Concept Drift

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
Time-Series ForecastingDistribution ShiftConcept Drift
Abstract:

Time-series forecasting finds broad applications in real-world scenarios. Due to the dynamic nature of time series data, it is important for time-series forecasting models to handle potential distribution shifts over time. In this paper, we initially identify two types of distribution shifts in time series: concept drift and temporal shift. We acknowledge that while existing studies primarily focus on addressing temporal shift issues in time series forecasting, designing proper concept drift methods for time series forecasting has received comparatively less attention.

Motivated by the need to address potential concept drift, while conventional concept drift methods via invariant learning face certain challenges in time-series forecasting, we propose a soft attention mechanism that finds invariant patterns from both lookback and horizon time series. Additionally, we emphasize the critical importance of mitigating temporal shifts as a preliminary to addressing concept drift. In this context, we introduce ShifTS, a method-agnostic framework designed to tackle temporal shift first and then concept drift within a unified approach. Extensive experiments demonstrate the efficacy of ShifTS in consistently enhancing the forecasting accuracy of agnostic models across multiple datasets, and outperforming existing concept drift, temporal shift, and combined baselines.

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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.
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Overview

Overall Novelty Assessment

The paper proposes ShifTS, a framework addressing both temporal shift and concept drift in time-series forecasting through a soft attention mechanism (SAM) that identifies invariant patterns. It resides in the 'Invariant Pattern Learning' leaf, which contains only two papers including this one. This leaf sits within the broader 'Invariant and Causal Learning for Distribution Shifts' branch, indicating a relatively sparse research direction compared to more crowded areas like normalization-based methods or online adaptation. The focus on concept drift mitigation through invariant learning represents a less-explored angle in the field.

The taxonomy reveals that neighboring approaches pursue different philosophies: normalization-based methods (Instance Normalization, Invertible Neural Networks) transform data into stable spaces, while online adaptation branches (Drift Detection, Continuous Learning) emphasize dynamic model updates. The paper's invariant learning approach contrasts with these by seeking stable patterns rather than normalizing distributions or adapting reactively. The 'Unified Frameworks for Multiple Shift Types' category exists as a separate branch, suggesting that combining temporal shift and concept drift handling—as ShifTS attempts—is recognized as distinct from single-focus methods.

Among thirty candidates examined, the contribution identifying two distribution shift types (temporal shift and concept drift) shows two refutable candidates, indicating this conceptual distinction has prior articulation in the literature. However, the SAM mechanism and the ShifTS framework itself show zero refutable candidates across ten examined papers each. This suggests that while the problem framing has precedent, the specific technical approach—using soft attention to find invariant patterns across lookback and horizon windows—appears less directly overlapped by the limited candidate set examined. The framework's method-agnostic design and sequential handling of shift types may differentiate it from existing work.

Based on the top-30 semantic search scope, the paper appears to occupy a relatively novel position within invariant learning approaches to distribution shifts. The limited sibling papers in its taxonomy leaf and absence of clear refutations for its core technical contributions suggest distinctiveness, though the conceptual framing of shift types has documented precedents. A broader literature search might reveal additional related work in adjacent categories like representation alignment or unified frameworks.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
30
Contribution Candidate Papers Compared
2
Refutable Paper

Research Landscape Overview

Core task: Mitigating distribution shifts in time-series forecasting. The field addresses the challenge that real-world time series often exhibit non-stationarity, concept drift, and evolving patterns that degrade model performance when training and test distributions diverge. The taxonomy reveals a rich landscape organized around several complementary strategies. Normalization-based methods such as Reversible Instance Normalization[4] and Invertible Neural Transformation[5] attempt to stabilize inputs by removing or modeling distributional changes explicitly. Invariant and causal learning approaches, including Invariant Learning Forecasting[21], seek patterns that remain stable across different regimes. Online adaptation and concept drift detection branches focus on dynamic model updates and drift identification, while representation alignment methods like Distribution Aware Alignment[3] and Dish-TS[2] aim to harmonize feature spaces across shifting conditions. Meta-learning and hypernetwork-based techniques, exemplified by Hypernetworks Distribution Shift[8], enable rapid adaptation to new distributions, and patch-level modeling captures localized temporal structures that may be more robust to global shifts. Several active research directions reveal contrasting philosophies and open questions. One line emphasizes proactive normalization and feature engineering to preemptively reduce shift impact, as seen in works like Instance Normalization Flows[7] and Evolving Multi-Scale Normalization[27]. Another prioritizes learning invariant representations that generalize across domains, with Concept Drift Mitigation[0] situated within this invariant pattern learning cluster alongside Invariant Learning Forecasting[21]. Compared to normalization-centric approaches that transform data into a stable space, Concept Drift Mitigation[0] and its neighbors focus on identifying and leveraging causal or invariant structures that persist despite distributional changes. Meanwhile, online adaptation methods such as Detect then Adapt[12] and Shift Aware Test Time[33] offer a third perspective, continuously updating models in response to detected shifts rather than seeking universal invariance. The interplay between these strategies—whether to normalize away shifts, learn invariant features, or adapt dynamically—remains a central theme, with many recent efforts exploring hybrid frameworks that combine multiple mitigation principles.

Claimed Contributions

Soft Attention Masking (SAM) for Concept Drift Mitigation

The authors introduce SAM, a soft attention masking mechanism designed to mitigate concept drift in time-series forecasting by identifying invariant patterns across both lookback and horizon windows of exogenous features, enabling the model to learn stable conditional distributions.

10 retrieved papers
ShifTS Framework for Unified Distribution Shift Handling

The authors propose ShifTS, a model-agnostic framework that addresses both temporal shift and concept drift in time-series forecasting by first normalizing data to handle temporal shifts, then applying SAM to address concept drift, all within a unified two-stage forecasting process.

10 retrieved papers
Identification of Two Distribution Shift Types in Time Series

The authors formally distinguish and define two types of distribution shifts affecting time-series forecasting: concept drift (changing conditional distributions) and temporal shift (changing marginal distributions), highlighting that existing work primarily addresses temporal shift while concept drift remains underexplored.

10 retrieved papers
Can Refute

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Soft Attention Masking (SAM) for Concept Drift Mitigation

The authors introduce SAM, a soft attention masking mechanism designed to mitigate concept drift in time-series forecasting by identifying invariant patterns across both lookback and horizon windows of exogenous features, enabling the model to learn stable conditional distributions.

Contribution

ShifTS Framework for Unified Distribution Shift Handling

The authors propose ShifTS, a model-agnostic framework that addresses both temporal shift and concept drift in time-series forecasting by first normalizing data to handle temporal shifts, then applying SAM to address concept drift, all within a unified two-stage forecasting process.

Contribution

Identification of Two Distribution Shift Types in Time Series

The authors formally distinguish and define two types of distribution shifts affecting time-series forecasting: concept drift (changing conditional distributions) and temporal shift (changing marginal distributions), highlighting that existing work primarily addresses temporal shift while concept drift remains underexplored.

Table of Contents