Title: Learning with Calibration: Exploring Test-Time Computing of Spatio-Temporal Forecasting
Venue: NeurIPS 2025
Accepted: yes
Keywords: Spatio-Temporal Forecasting

Open-World Learning
Abstract: Spatio-temporal forecasting is crucial in many domains, such as transportation, meteorology, and energy. However, real-world scenarios frequently present challenges such as signal anomalies, noise, and distributional shifts. Existing solutions primarily enhance robustness by modifying network architectures or training procedures. Nevertheless, these approaches are computationally intensive and resource-demanding, especially for large-scale applications. In this paper, we explore a _novel **t**est-**t**ime **c**omputing paradigm, namely learning with calibration, **ST-TTC**_, for **s**patio-**t**emporal forecasting. Through learning with calibration, we aim to capture periodic structural biases arising from non-stationarity during the testing phase and perform real-time bias correction on predictions to improve accuracy. Specifically, we first introduce a spectral-domain calibrator with phase-amplitude modulation to mitigate periodic shift and then propose a flash updating mechanism with a streaming memory queue for efficient test-time computation. _**ST-TTC**_ effectively bypasses complex training-stage techniques, offering an efficient and generalizable paradigm. Extensive experiments on real-world datasets demonstrate the effectiveness, universality, flexibility and efficiency of our proposed method.


Peer reviews:
comments: SUMMARY:
This work proposes ​ST-TTC, a novel test-time computing paradigm for spatio-temporal forecasting that dynamically corrects prediction biases during inference, and conduct effective experiments on real-world evaluation datasets.

STRENGTHS AND WEAKNESSES:
Strength 
1. Calibration is a light-weighted test-time training method, and a good solution for maintaining a long-term deloyed spatial-temporal prediction model.
2. Calibrate in spectral space sounds promising.
3. The method acquires universal performance gain across multiple real-world benchmarks.

Weaknesses
1. The paper discuss the difference between test-time training and other solutions for OOD prediction, but does not conduct experiments comparing these methods. The authors need to answer with experimental results: "In what case, we need to consider test-time training instead of CT or Online CT etc. ?"
2. The paper report the relative improvement ratios, but the absolute improvements seems not so significant.

QUESTIONS:
1. Video prediction is widely adopted task format for spatio-temperol prediction, especially in met and traffic domains, does ST-TTS works in video prediction tasks?

2. Apart form period shift, can ST-TTS face other OOD challenges, like corrupted input data, incident/extreme cases ?

LIMITATIONS:
yes
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-07-01

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces Spatio-Temporal Test-Time Computing (ST-TTC), a novel and lightweight calibration framework for spatio-temporal forecasting that addresses challenges such as non-stationarity and distribution shifts without modifying the backbone model or requiring additional training data. The proposed method incorporates a Spectral Domain Calibrator, which mitigates periodic biases in the frequency domain through phase-amplitude modulation, alongside a Flash Gradient Update mechanism utilizing a Streaming Memory Queue to efficiently adapt calibration parameters during inference. Extensive experiments across diverse benchmarks consistently demonstrate that ST-TTC enhances forecasting accuracy over strong baselines while incurring minimal computational overhead.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths 1.\tThe paper addresses an important and practical problem in spatio-temporal modeling. Non-stationary data is prevalent and challenging, and proposing a generalizable framework to leverage test-time data is a promising direction.\n2.\tThe use of spectral domain calibration with phase-amplitude modulation is theoretically grounded, interpretable, and empirically shown to effectively mitigate non-stationary distribution shifts to a certain extent.\n3.\tThe experimental evaluation is thorough, covering a broad range of models, datasets, and scenarios, including few-shot, long-term, large-scale, out-of-distribution (OOD), and continual learning settings, demonstrating the method’s versatility.\n\n⸻\n\nWeaknesses\n1.\tThe proposed approach focuses on periodic biases and may not generalize well to non-periodic or abrupt structural changes commonly observed in spatio-temporal data.\n2.\tAlthough the method is designed to handle OOD scenarios, the paper lacks a formal and quantitative definition of OOD in the spatio-temporal context, and the degree of distribution shifts present in the evaluated datasets remains unclear.\n\nQUESTIONS:\nThe experimental evaluation is comprehensive. However, my main concern lies in the performance under OOD conditions. Since the extent of distribution shifts in the datasets is not quantified, it remains unclear how effectively the proposed method can handle truly OOD scenarios. In particular, spatio-temporal distribution shifts can manifest at both structural and temporal levels. While Figure 5 offers some insight into this, the degree of shift introduced by new nodes is not explicitly presented. It would be very helpful for the authors to clarify the following points:\n\n1. To what extent do temporal feature distribution shifts exist in the datasets used? It would be beneficial to quantify the degree of such shifts using appropriate metrics or visualizations.\n\n2. Does the current framework adapt to structural shifts such as changes in node homophily or the overall graph topology?\n\n3. Does the proposed framework support dynamic graphs with evolving structures over time?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

comments: SUMMARY:
The paper proposed a novel test-time computing paradigm for spatio-temporal forecasting, ST-TTC, where a plug-in calibration module is added between the base-model prediction and the final predictions. This calibration module improves performance at sporadic non-stationary spatio-temporal behavior of the data without expensive training. The method is evaluated on traffic, meteorological, and energy datasets. ST-TTC shows promising improvement in performance by efficiently using the computing resources.

STRENGTHS AND WEAKNESSES:
Strengths:
1) The codebase is available and documented for review.
2) The introduction motivates the problem very well.
3) Figure 1 clearly depicts the difference between test-time training and online continual learning.
4) Related work is well placed.
5) The paper explains motivation, key challenges, implementation details with mathematical details, complexity, and theoretical analysis for the methodical design choices.
6) Tested on six publicly available datasets with Transformer-based, Graph-based, and MLP-based base models. Also outperforms the baselines in most scenarios.
7) The evaluation shows improvement (%) for different base-model architectures.
8) ST-TTC is efficient and robust, as shown in the results
9) Clearly mentioned the observed limitations and future directions.

Weakness:
1) Missing an Overview diagram for the methodology. I suggest including a pictorial representation of the methodology.

QUESTIONS:
None

LIMITATIONS:
1) Lack of testing on spatio-temporal foundation models
2) Does not significantly improve performance on small spatio-temporal benchmarks
TITLE: Official Review
CLARITY: 4
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-07-03

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nAddressing distribution shift in spatio-temporal data requires some form of adaptation at test time since we rarely see all possible changes during training stages. The work thus proposes ST-TTC, which calibrates the model output in the frequency space through a learned parameter-parsimonious transformation. The adaptation module is continuously updated during inference. The experiments confirm the method's effectiveness and efficiency.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths:**\n- The writing is of very high quality and clarity, with exceptional Sections 4 & 5.\n- The problem is significant, and the solution is not very original, yet possibly novel.\n- The method is conceptually simple.\n- While I am not sufficiently familiar with the field to judge the overall novelty or the completeness of datasets and baselines, their volume appears rather comprehensive.\n\n**Opportunities for improvement (weaknesses):**\n- It is unclear how the offsets $\\lambda^\\alpha$ and $\\lambda^\\phi$ are initialized and learned before inference time from the main text. Pages 21-22 clarify this, but it should be stated more clearly that there is no training stage in the main paper.\n- Regarding ll. 287ff: The long-term setting is set up for only 24-to-24 time steps. However, recently, long-term time series forecasting often looks at hundreds-to-hundreds (see, e.g., Wu et al. (2022) and many more recent works). Possibly, this is substantially different in spatio-temporal forecasting.\n- Regarding Thm. 1 in App. B.1: The theorem claims an exact upper bound, but the proof relies on a first-order Taylor expansion, which only provides an approximate result. To be rigorous, the theorem should either state that the bound is approximate or the proof should account for the higher-order terms.\n- Checklist item 7 is answered with \"yes\", yet the work only provides variance measurements and no statistical tests.\n\n**Minor Comments:**\n- Fig. 3 (left): Why are the lines connected across datasets? This suggests a connection that is not there. A bar plot might be more appropriate.\n- L. 366: \"noval\" -> \"novel\"\n- Ll. 874-886 are a summary of the entire work and are misplaced there. They should get deleted.\n- Regarding Thm. 1 in App. B.1: The result gets weakened in the very last step (last \"$\\leq$\") of the proof, making the bounds less tight than possible. In fact, regarding Remark 1, the bounds are actually sub-linear in the modulation parameters.\n\n**References:**\n- Wu, H., Xu, J., Wang, J., & Long, M. (2022). Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting. arXiv. https://doi.org/10.48550/arXiv.2106.13008\n\nQUESTIONS:\nQ1: Regarding l. 351, \"fewer groups generally lead to poorer performance\": Fig. 7 (middle) shows rather constant behaviour for varying $m$, much like $n$ in Fig. 7 (right). How was the cited statement derived from those figures?\n\nLIMITATIONS:\nNothing is provided in the main text, which is unfortunate. However, Appendix F.1 discusses limitations in great detail.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-03"}