Title: TimeEmb: A Lightweight Static-Dynamic Disentanglement Framework for Time Series Forecasting
Venue: NeurIPS 2025
Accepted: yes
Keywords: Time series forecasting

Frequency domain analysis

Disentangled representation learning
Abstract: Temporal non-stationarity, the phenomenon that time series distributions change over time, poses fundamental challenges to reliable time series forecasting. Intuitively, the complex time series can be decomposed into two factors, i.e., time-invariant and time-varying components, which indicate static and dynamic patterns, respectively. Nonetheless, existing methods often conflate the time-varying and time-invariant components, and jointly learn the combined long-term patterns and short-term fluctuations, leading to suboptimal performance facing distribution shifts. To address this issue, we initiatively propose a lightweight static-dynamic decomposition framework, TimeEmb, for time series forecasting. TimeEmb innovatively separates time series into two complementary components: (1) time-invariant component, captured by a novel global embedding module that learns persistent representations across time series, and (2) time-varying component, processed by an efficient frequency-domain filtering mechanism inspired by full-spectrum analysis in signal processing. Experiments on real-world datasets demonstrate that TimeEmb outperforms state-of-the-art baselines and requires fewer computational resources. We conduct comprehensive quantitative and qualitative analyses to verify the efficacy of static-dynamic disentanglement. This lightweight framework can also improve existing time-series forecasting methods with simple integration. To ease reproducibility, our code is available at https://github.com/showmeon/TimeEmb.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper introduces an efficient and novel time series forecasting framework TimeEmb. It disentangles time series into static and dynamic components, processed by a learnable embedding bank and a frequency filter, respectively. Extensive experimental results suggest improved performance and efficiency. TimeEmb can also enhance existing methods with trivial extra trainable parameters.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n-\tTimeEmb pioneers the use of a learnable global embedding bank to capture recurrent and time-invariant patterns across the entire dataset. This approach avoids the rigid assumptions of pre-defined periodicities found in methods like CycleNet.\n\n-\tThe framework innovatively processes the time-varying component through an efficient frequency-domain filtering mechanism, which is both effective and efficient.\n\n-\tThe model consistently achieves state-of-the-art or highly competitive performance across seven real-world benchmark datasets and various prediction horizons. \n\n-\tTimeEmb exhibits remarkable compatibility, consistently improving the performance of various existing MLP-based and Transformer-based forecasting models when integrated as a plug-in.\n\n\nWeaknesses:\n\n-\tWhile the embedding bank is claimed to capture \"global recurrent features\", the description also notes it \"provides specific embedding for individual timesteps\"  and \"time-invariant patterns may differ at different timesteps\". This creates a conceptual ambiguity about the precise nature of the \"time-invariant\" component being learned, blurring the distinction between a truly global pattern and a collection of localized periodic patterns.\n\n-\tThe paper does not clearly specify how the optimal value of loss weight $\\alpha$ is determined for each dataset or scenario.\n\n-\tIt would enhance the empirical verification to analyze the performance under a distribution-shifting setting.\n\nQUESTIONS:\n-\tThe paper focuses on non-stationarity due to changing trends and external interventions. How would TimeEmb perform on time series characterized by highly irregular or chaotic patterns where traditional periodic assumptions might not hold, or where infrequent, large-magnitude events dominate?\n\n-\tCould the authors provide a conceptual diagram or pseudocode illustrating the integration mechanism for TimeEmb when it functions as a ‘plug-in’ module for existing backbone models?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 4, "ORIGINALITY": 4, "DATE": "2025-06-26"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes TimeEmb, a lightweight framework for time series forecasting by disentangling static and dynamic components. It employs a learnable embedding bank for time-invariant patterns and a frequency filter for time-varying components, primarily operating in the frequency domain. The paper highlights its efficiency and effectiveness on various real-world datasets and its plug-and-play capability.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\nS1. This paper proposes a well-motivated disentanglement for time series prediction. The static-dynamic decomposition is a straightforward approach to model long-term patterns separate from short-term fluctuations. Beyond existing research which concatenates learnable time-in-day embeddings, the embedding bank of TimeEmb shares the same embedding space with time series and maintains the common pattern shared across the whole dataset. The well-designed disentanglement representation learning approach yields consistently superior performance beyond diverse state-of-the-art baselines.\n\nS2. The proposed TimeEmb is both effective and efficient. TimeEmb's lightweight architecture is a significant advantage, showcasing competitive performance with fewer parameters and lower training times compared to many strong baselines. \n\nS3. The paper is well-written and generally clear. The figures, especially Figure 1 illustrating performance vs. efficiency, are highly informative and well-designed. The methodology section provides a good overview of the framework and its components. Equations are presented clearly, and the high-level algorithm is provided in Appendix B. The experimental setup and implementation code are detailed, aiding reproducibility. \n\n\\\nWeaknesses:\n\nW1. Absence of statistical significance reporting for main results: The paper presents mean MSE/MAE values but lacks error bars or statistical significance tests in Tables 1 and 2. While the authors specify averaging over five runs, showing the variance (e.g., standard deviation) would greatly strengthen the empirical claims.\n\nW2. The ablation study shows that removing $H_{\\omega}$ leads to notable degradation. However, the paper doesn't delve deeper into how the learnable $\\omega$ adapts across different datasets or non-stationary patterns.\n\nW3. There are many related papers on time series forecasting. More through discussion and comparison with these papers are encouraged to be included in the paper.\n\nQUESTIONS:\nQ1. The paper uses ``Instance Normalization’’ at the beginning of the framework. Could the authors elaborate on the impact of this choice compared to other normalization techniques or no normalization, especially in the context of non-stationary time series?\n\nQ2. Please response to the comments in Weaknesses.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 4, "DATE": "2025-06-26"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces TimeEmb, a novel and highly effective framework for time series forecasting that addresses temporal non-stationarity by disentangling static (time-invariant) and dynamic (time-varying) components. The core innovation lies in using a learnable global embedding bank for static patterns and an efficient frequency-domain filter for dynamic fluctuations, all primarily operating within the frequency domain. The experimental results are compelling, demonstrating state-of-the-art performance with significantly reduced computational resources, and a remarkable plug-and-play compatibility with existing models. The paper also provides insightful visualizations supporting its disentanglement claims.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths:**\n\n- Proposes a novel disentanglement framework that separates static and dynamic components, addressing limitations in prior time series models.\n- Introduces a learnable global embedding bank for time-invariant patterns, improving over traditional local trend-season decompositions.\n- Demonstrates strong empirical performance across diverse datasets and baselines, with better efficiency (e.g., significantly fewer parameters than iTransformer).\n\n**Weaknesses:**\n\n- The time embedding bank requires manual tuning of $M$ based on domain knowledge. A fully adaptive, data-driven approach to learning multi-scale structures would improve applicability to real-world time series with nested or irregular periodicities.\n\nQUESTIONS:\n1. The authors employ a fixed-resolution embedding bank to represent time-invariant components. Beyond the current approach of manually adjusting the number of basis vectors ($M$), have the authors explored or considered mechanisms for adaptively learning or inferring optimal multi-scale granularities during training? Such an approach could provide a more holistic solution to the acknowledged limitations related to static representation flexibility.\n\n2. Figures 3 and 4 provide qualitative visualizations to illustrate the separation between static and dynamic components, suggesting improved disentanglement. To further substantiate the interpretability claims, could the authors elaborate on the specific types of insights or actionable information these disentangled components yield in real-world time series applications? For instance, how might practitioners leverage the static vs. dynamic decomposition in forecasting, anomaly detection, or downstream decision-making tasks?\n\nLIMITATIONS:\nSee **Weaknesses** and **Questions**.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a lightweight static-dynamic decomposition framework for time series forecasting. To obtain optimal performance in the face of distribution shifts, the proposed method learns to separate long-term patterns and short-term fluctuations by separating the time series into two complementary components. The authors demonstrate superior performance and storage efficiency on seven diverse benchmark datasets and functions as an efficient plug-in to enhance existing methods with minimal computational cost.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\nS1) The proposed method achieves competitive accuracy compared to state-of-the-art approaches, despite its simple architecture that does not rely on mechanisms such as attention.\nS2) The method is quantitatively evaluated against several recent baselines using benchmark datasets, demonstrating improved performance in time series forecasting. Additionally, ablation studies are conducted to validate the contribution of each component of the proposed method.\nS3) The source code is publicly available, allowing for detailed inspection and verification of the implementation.\n\n- Weaknesses\nW1) The rationale for fusing the separated time-invariant and time-variant components in the feature space remains unclear. While Section 4.5 emphasizes the importance of high-amplitude or low-frequency components in future prediction, this does not sufficiently justify why the static time-invariant and dynamic time-varying components should be recombined.\nW2) The authors criticize an existing method (CycleNet) for assuming a fixed periodic pattern in the dataset, stating that this assumption may not hold under complex or variable periodicities. However, the proposed method similarly relies on a user-defined value of *M* to impose periodicity, and thus suffers from the same limitation—namely, the inability to adapt to varying or complex periodic patterns.\nW3) There is a discrepancy between the model described in the paper and the implementation provided in the submitted code. In particular, for the computation described in Section 3.2, the code applies different processing to the real and imaginary parts, yet this distinction is not mentioned in the main text.\nW4) The authors claim that their method can be easily extended to enhance existing approaches. However, the paper lacks concrete explanation or examples of how such extensions would be implemented, leaving open the question of whether the method is truly straightforward to apply in practice.\n\nQUESTIONS:\nQ1) The proposed method captures time-invariant components through an embedding bank and utilizes them to separate time-variant components. However, the method fuses the two separated components just before the model’s output layer.  In contrast, other existing methods, such as Fredformer and DLinear, typically transform each separated feature back into the time domain before performing fusion. Is there a logical or theoretical reason why the proposed method does not adopt a similar design?\nQ2) Could the authors clarify how the parameter *M* is determined in each experiment? Is it selected based on prior knowledge, validation performance, or a fixed heuristic?\nQ3) While the paper claims that the proposed method can be easily extended to existing models, Table 3 shows only four base models being expanded, and only a subset of datasets used in Table 1 are included. Do the authors have additional experimental results (as seen in appendices for other experiments) demonstrating broader compatibility across more datasets and more base models?\nQ4) In Figure 4(b), the time-variant components extracted by the proposed method show clear similarities within the same day of the week and are distinguishable across different days. However, the authors describe the time-variant components as reflecting local fluctuations in the time series, such as anomalies caused by unusual weather or traffic incidents. If such components are indeed day-specific and recurring, wouldn’t it be more appropriate for this structure to emerge in the feature space of the time-invariant components instead? How do the authors reconcile this discrepancy between their definition and the observed clustering pattern?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-03"}