Title: How Different from the Past? Spatio-Temporal Time Series Forecasting with Self-Supervised Deviation Learning
Venue: NeurIPS 2025
Accepted: yes
Keywords: Spatio-Temporal Forecasting; Deviation Learning;
Abstract: Spatio-temporal forecasting is essential for real-world applications such as traffic management and urban computing. Although recent methods have shown improved accuracy, they often fail to account for dynamic deviations between current inputs and historical patterns. These deviations contain critical signals that can significantly affect model performance. To fill this gap, we propose $\textbf{ST-SSDL}$, a $\underline{S}$patio-$\underline{T}$emporal time series forecasting framework that incorporates a $\underline{S}$elf-$\underline{S}$upervised $\underline{D}$eviation $\underline{L}$earning scheme to capture and utilize such deviations. ST-SSDL anchors each input to its historical average and discretizes the latent space using learnable prototypes that represent typical spatio-temporal patterns. Two auxiliary objectives are proposed to refine this structure: a contrastive loss that enhances inter-prototype discriminability and a deviation loss that regularizes the distance consistency between input representations and corresponding prototypes to quantify deviation. Optimized jointly with the forecasting objective, these components guide the model to organize its hidden space and improve generalization across diverse input conditions. Experiments on six benchmark datasets show that ST-SSDL consistently outperforms state-of-the-art baselines across multiple metrics. Visualizations further demonstrate its ability to adaptively respond to varying levels of deviation in complex spatio-temporal scenarios. Our code and datasets are available at https://github.com/Jimmy-7664/ST-SSDL.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis submission introduces a spatio-temporal time series forecasting framework, ST-SSDL, which is based on a GCRU-based encoder-decoder and adopts Self-Supervised Deviation Learning (SSDL) to explicitly model dynamic deviations between current observations and historical trends using a self-supervised mechanism. The authors evaluated the proposed method on six traffic datasets. Quantitative benchmarks, ablation studies, efficiency metrics, and qualitative visualizations demonstrate the effectiveness of ST-SSDL.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\ni) The paper focuses on explicit deviation modeling in spatio-temporal forecasting, and proposes a principled self-supervised approach to quantify and leverage deviations; \nii) The use of prototypes in latent space adds a structured, interpretable layer to the self-supervised learning pipeline; \niii) The proposed model is benchmarked on six well-known datasets and compared against a wide range of SOTA baselines. Ablation and efficiency studies are presented clearly; \niv) Prototype-based modeling provides interpretability in the latent space, and the visualizations (Figures 4 and 5) effectively demonstrate how the model adjusts to varying deviation levels; \nv) The authors stated to release code and data, which supports reproducibility.\n\nWeaknesses\ni) The focus of the paper is the introduction of SSDL, which could be applicable in many dynamic prediction tasks. It is not clear how it may perform on different architectures besides GCRU; \nii) It is not clear how spatial relationships are captured or leveraged in ST-SSDL; \niii) The experiments are all based on traffic datasets. It is unclear how generalizable the proposed method is to other spatio-temporal domains (e.g., climate, mobility, energy);\niv) Although the model is lightweight in model parameters, inference latency is relatively high due to the GCRU backbone. This limits deployment in real-time applications; \nv) There seem to be inconsistent notations: notation inconsistency: The authors used $X^t$ (e.g., first appeared in line 103 and throughout Section 4) to denote the recent input sequence $X_{\\tau−T+1:\\tau}$, but this superscript notation was never clearly defined and may be confused with a time index. Since $\\tau$ was already used to indicate the current time, a more consistent and intuitive notation might be $X^\\tau$ or simply $X_{t-T+1:t}$? \nvi) In Section 5.6, Figure 4 was not really referred or discussed in text.\n\nQUESTIONS:\n1. Since the paper focuses on spatio-temporal time series forecasting, which component in ST-SSDL specifically addresses spatial analysis? \n\n2. How the anchors were computed in Section 4.1? \n\n3. Have the authors considered applying ST-SSDL to other spatio-temporal domains such as weather, electricity, or medical time series? \n\n4. Can you provide further semantic interpretation of the prototypes? Do they correspond to interpretable traffic states (e.g., rush hour, low traffic)?\n\nLIMITATIONS:\nThe authors did not explicitly address the broader limitations or potential societal impacts. Some brief discussions on: i) Applicability to high-risk domains; and ii) Limitations in the anchoring mechanism; could be added.", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-06-27"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper introduces ST-SSDL, a spatio-temporal forecasting framework leveraging Self-Supervised Deviation Learning (SSDL) to capture dynamic deviations between current inputs and historical patterns. ST-SSDL anchors inputs to historical averages, discretizes the latent space with learnable prototypes, and optimizes using contrastive and deviation losses. Experiments on six benchmark datasets show it outperforms existing methods.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n1.\tThe motivation is clearly articulated: The paper identifies a critical gap in existing spatio-temporal models—ignoring dynamic deviations between current inputs and historical patterns.\n\n2.\tThe proposed ST-SSDL mechanism, including historical anchoring and prototype-based discretization, provides strong interpretability. Visualizations (Fig. 4-5) demonstrate adaptive prototype assignments under varying deviation levels, enhancing model transparency.\n\nWeaknesses:\n\n1.\tThe baselines used for comparison are outdated. While the paper references more recent models, these are not included as baselines, e.g., the meta-learning framework HIMeta [13] from KDD 2024.\n\n2.\tThe Query-Prototype mechanism closely resembles VQ-VAE’s codebook design, with attention-based soft assignment (Equation (3)) replacing hard quantization. The paper lacks a thorough discussion on how this differs from or improves upon VQ-VAE.\n\n3.\tThe use of averaging to generate historical anchors may be overly simplistic. The paper does not explore or compare alternative dynamic anchoring strategies\n\n4 . What is the difference between \"dynamic deviations\" and \"distribution shift\"? Can you cite literature to explain \"dynamic deviations\"?\n\nQUESTIONS:\n1.\tSee Weakeness\n2.\tIn Equation (4) and Equation (5), one loss uses MSE while the other uses MAE. What is the rationale behind this choice?\n\nLIMITATIONS:\nSee above", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 2, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes ST-SSDL, a framework for forecasting spatio-temporal time series by explicitly modeling how current patterns deviate from historical trends. ST-SSDL uses a self-supervised approach: it compares current input data to its historical average, then represents both in a structured latent space using learnable prototypes that capture typical patterns. Two additional training objectives help the model quantify and organize these deviations without needing labeled data. Experiments on six benchmark datasets show that ST-SSDL consistently outperforms existing methods in prediction accuracy and adapts well to changing conditions.\n\nSTRENGTHS AND WEAKNESSES:\nThe paper is well-written and methodologically rigorous, introducing a novel self-supervised framework (ST-SSDL) that effectively models deviations in spatio-temporal data. It demonstrates strong empirical results across six benchmarks, supported by thorough ablation studies and clear visualizations, while maintaining reproducibility through open-sourced code and detailed experimental setups.\nWhile technically sound, the paper has several limitations: (1) its reliance on historical averages as anchors isn't tested against noisy or non-stationary data (e.g., sudden traffic disruptions); (2) the 20 prototypes, while empirically chosen, lack interpretability as real-world patterns; (3) evaluation is confined to periodic traffic data, leaving generalization to irregular domains (e.g., epidemiology) unverified;\n\nQUESTIONS:\n1. The paper assumes historical averages are reliable anchors, but real-world data often contains outliers or non-stationary trends (e.g., sudden traffic disruptions). Could ST-SSDL integrate robust statistics or outlier detection to improve deviation modeling in such cases?\n2. The experiments focus on traffic data with stable periodic patterns. How might ST-SSDL need to be modified for domains with irregular or aperiodic deviations (e.g., disease spread or social media activity)?\n3. Have the authors analyzed whether learned prototypes correspond to interpretable or human-understandable patterns?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 2, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}

comments: SUMMARY:
The paper introduces a method to consider past events through a latent space that is then integrated into the learning process. The authors present experimental data that they claim supports their insight.

STRENGTHS AND WEAKNESSES:
Strengths:

the idea that you can take advantage of re-occurring irregularities makes good sense. For example, a car mightperform differently in a highway or in busy city  street.
\\the evaluation seems to support these claims

Weaknesses
The authors present the work in a very abstract way. An example would have make things  clear, and would motivate the work.
Fig 1: I couldn;t quite understand.
The results are extensive but for a very specific type of domains

QUESTIONS:
into  S non-overlapping weekly ->why weekly? Shouldn´t this be application dependent?

. Experiment statistical significance-.> I do not understand tthat reply, can´t you use a fixed number of seeds?

- with dimensions h set to 128, 64,
242 or 32, depending on the dataset."
do you have a rule for this choice?

figure 5: can you move back from the latent space to the original data?

LIMITATIONS:
...
TITLE: Official Review
CLARITY: 2
IMPACT: 4
ORIGINALITY: 3
DATE: 2025-07-05