Title: DecompNet: Enhancing Time Series Forecasting Models with Implicit Decomposition
Venue: NeurIPS 2025
Accepted: yes
Keywords: Time series forecasting

Time series enhancement framework

Time series decomposition

Deep learning
Abstract: In this paper, we pioneer the idea of implicit decomposition. And based on this idea, we propose a powerful decomposition-based enhancement framework, namely DecompNet. Our method converts the time series decomposition into an implicit process, where it can give a time series model the decomposition-related knowledge during inference, even though this model does not actually decompose the input time series. Thus, our DecompNet can enable a model to inherit the performance promotion brought by time series decomposition but will not introduce any additional inference costs, successfully enhancing the model performance while enjoying better efficiency. Experimentally, our DecompNet exhibits promising enhancement capability and compelling framework generality. Especially, it can also enhance the performance of the latest and state-of-the-art models, greatly pushing the performance limit of time series forecasting. Through comprehensive comparisons, DecompNet also shows excellent performance and efficiency superiority, making the decomposition-based enhancement framework surpass the well-recognized normalization-based frameworks for the first time. Code is
available at this repository: https://github.com/luodhhh/DecompNet.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a novel time series forecasting method centered on trend-seasonality re-parametrization, introducing a new concept referred to as \"implicit decomposition.\" The proposed approach demonstrates consistent improvements across various baselines.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n- The proposed model shows strong performance gains across a wide range of datasets and baselines, as reflected in the presented tables and figures. Table 7 in the appendix nicely summarizes the model's improvements.\n\n- The figures are clear and convey the findings well. Figure 4 provides an effective visual comparison of benchmarks for multiple factors, including loss, inference time, and varying input lengths.\n\n- Forecast examples included in Appendix N.1 and N.2 help showcase the performance improvement.\n\n- An ablation study on each component of the method is provided to showcase the combined improvement of the proposed techniques.\n\nWeaknesses:\n1. The reference to trend-seasonality re-parameterization as “implicit decomposition” is potentially misleading. In practice, the model performs explicit decomposition during training using a moving average filter. Implicit decomposition is assumed to occur during inference through the re-parameterized model; however, there is no clear evidence or analysis confirming that this actually happens. This behavior seems to be inferred from the empirical forecasting results.\n\n2. Many of the claims throughout the paper use subjective or vague language where quantitative evidence is not explicitly provided to verify them. The reader must try to parse the claims from the provided figures and tables. I recommend that the authors revise these statements and explicitly tie each claim to specific metrics results. Some examples include:\n   - Line 241: “by a large margin”\n   - Lines 261–262: “DecompNet also significantly surpasses...”\n   - Line 366: “enjoying better performance and having totally the same inference costs”\n\n3. The authors report the standard deviation of DecompNet performance under five runs with different random seeds in Table 6 in the Appendix using PatchTST, but do not do the same for the other benchmarks. The average results along with standard deviations are not included in the main tables. With results across random seeds for both the baseline and the proposed method (for each model), one can also consider adding statistical significance tests to confirm whether improvements are meaningful or due to chance.\n\n4. I understand the focus on inference cost for deployment practicality. However, including training cost information is still valuable to ensure the method incurs a reasonable expense. Training cost details are not explicitly conveyed in the main paper, though metrics like the number of epochs are used in the appendix. Including a couple of sentences on this in the main paper with explicit metrics would be helpful. Also, metrics like FLOPs and wall-clock time are generally used to convey computational cost and training time, respectively.\n\n5. For many key details, the reader is directed to refer to the appendix. For example, explanations of benchmarks like MICN and Leddam, details on the average window size for the proposed method, and references to model training details in Appendix C. Including a summary sentence on these topics before redirecting readers to the appendix would be helpful.\n\nOther:\n1. The trend-seasonality re-parameterization, which appears to be one of the main contributions of the paper, is not introduced until page 3. This core idea should be presented earlier, as it seems to be the main contribution and may be both clearer and more familiar to readers than the concept of implicit decomposition. The notion of implicit decomposition could then be introduced in relation to this concept.\n\n2. It would strengthen the paper to compare the proposed model and its variants against other prominent decomposition-based models such as DLinear, NHiTs, and N-BEATS. This comparison would better contextualize the method’s performance and practical relevance.\n\n3. Section G is helpful and addresses the effect of moving window size. This could be referenced in the main text to assure readers that this aspect has been considered and studied.\n\n4. Including some forecast examples in the main paper would be very helpful to demonstrate the performance improvement of the proposed approach.\n\nQUESTIONS:\n1. Is the lambda fusion parameter learned end-to-end during model training as a network weight, or is it tuned as a hyperparameter? If the latter, what is the range of values considered?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-06-21"}

comments: SUMMARY:
The authors propose training the seasonal module and trend module separately, and then fusing them into any time series model. However, the details of the model are not clearly explained, and treating different time series models merely as simple linear combinations is not appropriate.

STRENGTHS AND WEAKNESSES:
Strengths

The authors  pretrain two copies of it on the decomposed seasonal and trend data, helping them grasp  the decomposition-related knowledge and making them to be the seasonal and trend expert models.  Then we fuse these two expert models back as one single model, merging all of their decomposition related knowledge into the fused model. In this way, author make it to inject the decomposition related knowledge into a single model (i.e., the fused model).

weaknesses:
1. What are the network architectures of Model X and Model Y? 
2. Wt and Ws cannot be fused using Equation 10. On one hand, Xs and Xt are derived under a non-stationary mode. On the other hand, for Equation 10, the second equality does not hold—there are no parameters $\lambda$ that can make the two sides equal. 
3. In Fusion of complicated models module, the explanation lacks a detailed discussion on the fusion of the two, particularly  for models like patchTST and iTransformer, which do not fall under the category of simple linear models. 
4. In the introduction, the authors mention the implementation of inference costs; however, in the experiments, there is no detailed analysis of the inference costs after incorporating DecompNet.
5. The DecompNet section lacks an overall model architecture diagram. 
6. Including specific experimental results in the introduction and abstract would further enhance the clarity and impact.

QUESTIONS:
see weaknesses

LIMITATIONS:
yes
TITLE: Official Review
CLARITY: 2
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-06-30

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes DecompNet, a model-agnostic framework that enhances time series forecasting models via implicit decomposition. During training, two expert models are trained separately on the seasonal and trend components of the input series. These expert models are later fused into a single model using a learnable fusion factor, enabling inference on raw time series without requiring explicit decomposition at test time. Extensive experiments across 8 benchmark datasets and multiple backbone models (e.g., PatchTST, RLinear) show consistent improvements in accuracy. The authors also claim that DecompNet outperforms prior decomposition methods and normalization-based frameworks like RevIN, without increasing inference cost. The main contributions are: (1) introducing implicit decomposition, (2) proposing a decoupled-and-fusion training scheme, and (3) demonstrating consistent gains across model types and datasets\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n- Practical Innovation: Injects decomposition-related knowledge via a learnable fusion step, eliminating the inference overhead of prior decomposition-based frameworks.\n- Model-Agnostic Compatibility: Enhances diverse forecasting architectures (e.g., Transformer, MLP, CNN) without modifying their design.\n- Strong Empirical Rigor: Evaluates across 8 datasets and multiple baselines, with ablations and multiple seeds confirming consistency.\n- Efficiency + Effectiveness: Achieves notable performance gains without any added inference cost—which is rare among enhancement frameworks. The decomposition is done implicitly during training, so the final model runs as fast and light as the original, making it highly practical for real-world deployment.\n\nWeaknesses:\n- Limited Technical Novelty: The framework builds on existing ideas such as structural re-parameterization and training expert models on decomposed components. As a result, it may be seen more as a practical engineering extension rather than a fundamentally novel algorithmic breakthrough. The core idea – train separate models for trend and seasonal components and then merge – is clever but not entirely unprecedented.\n- Unclear Fusion Implementation: Although the paper outlines a high-level strategy for fusing expert models by aligning corresponding linear or convolutional layers, it does not clarify how architectural mismatches are handled, such as differences in model depth, or the presence of normalization layers and residual connections. Furthermore, it remains unclear whether the fusion weights (e.g., λ factors) are shared across all layers or learned independently for each layer. These omissions limit clarity and may hinder reproducibility.\n- Lack of Robustness to Test-Time Shifts: The method assumes stationary test distributions and currently has no mechanism to adapt to distribution shifts at inference (such as sudden level changes or variance shifts in the input data). Unlike normalization-based methods like RevIN or more adaptive frameworks, DecompNet does not explicitly address these forms of non-stationarity. This could make the approach vulnerable when the training and testing data distributions differ significantly.\n\nQUESTIONS:\n1.\tAre the fusion weights (λ factors) shared across all layers, or learned independently per layer?\nClarifying this would help assess the flexibility and generalization capability of the fusion strategy. A more detailed explanation—ideally supported by a schematic or pseudocode—would improve transparency and reproducibility.\n2.\tHow does DecompNet handle architectural mismatches during fusion? \nIf the expert models have different depths or contain non-matching layers (e.g., normalization, residual connections), how is the fusion performed? Does the fusion mechanism rely on strict layer-wise alignment, and how are such inconsistencies resolved?\n3.\tHow robust is DecompNet to non-stationary test-time inputs?\nWithout any mechanism to adjust for distribution shifts at inference time, how does the model perform when the input time series exhibits level shifts, changing trends, or variance fluctuations not seen during training? Is there any empirical evidence comparing its performance with methods like RevIN that explicitly account for such shifts?\n4.\tHow do the authors plan to address the scalability of DecompNet to more than two decomposition components?\nThe paper acknowledges as a limitation that the current implementation only supports seasonal and trend components and suggests future integration with methods like STL or frequency-domain decomposition. Could the authors elaborate on what specific technical challenges they foresee in extending DecompNet to handle additional components (e.g., residuals or noise)? For example, how might fusion complexity, training stability, or parameter efficiency be affected?\n\nLIMITATIONS:\nYes, the authors acknowledge a key limitation of their current implementation: it only supports decomposition into two components—seasonal and trend—and does not yet incorporate more advanced methods such as STL or frequency-domain decomposition. This limitation is clearly stated in Appendix L, along with a plan for future work. However, it would strengthen the paper if the authors also discussed the potential technical challenges involved in extending the framework to handle more than two components (e.g., residuals or noise), such as increased fusion complexity, parameter growth, or optimization difficulty. Addressing these considerations would provide a clearer picture of the method’s scalability.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 4, "ORIGINALITY": 3, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces a framework called DecompNet that implements implicit decomposition to improve time-series forecasting performance without added inference cost. The key idea is to train two copies of a base forecasting model on the decomposed seasonal and trend components of the data (so each becomes an expert in one component) and then fuse them into a single model via a novel Seasonal-Trend re-parameterization technique. During this fusion, the two expert networks’ parameters are merged (with a learned scaling factor) into one set of weights, yielding an enhanced model that can directly ingest raw time series and still leverage decomposition-related knowledge.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n- The paper introduces a new approach to time-series forecasting by injecting decomposition knowledge via training, rather than performing decomposition at inference. This idea of implicit decomposition is highly novel and addresses a gap, as it transforms decomposition into a model-agnostic enhancement that incurs no runtime cost.\n- DecompNet delivers consistent t forecasting improvements across a wide range of models and datasets.\n- The paper is well-written and logically organized.\n\nWeaknesses:\n\n- DecompNet’s benefits come at the cost of a more complex training pipeline. The method requires training two separate model instances on decomposed data and then performing an extra fusion fine-tuning stage. This means the total training time and computational resources are higher than for training a single model once. The authors do argue that the extra stages converge quickly, but the paper does not quantify the overhead.\n- The framework relies on a specific decomposition technique (moving average with fixed window, yielding one seasonal and one trend series). This choice might not be optimal for all data – for example, a window of 25 might not align with the true seasonality in certain datasets, or some time series might have multiple seasonalities or a large residual component. The current method does not adapt the decomposition to data characteristics (e.g., no automatic period detection).\n-  While the paper’s approach is empirically validated, it lacks a deeper theoretical explanation for why the fused model can perform as well as two separate models, especially given the non-linear nature of deep networks. The derivation in Equation (10) is a helpful intuition for linear layers, but it doesn’t formally guarantee performance preservation for complex networks – instead the authors rely on a training-based solution. The success of the fusion is demonstrated experimentally, but the paper does not analyze scenarios where it might fail or whether any bounds/guarantees exist.\n- The study focuses on one particular way of decomposing (seasonal/trend via moving average). It does not explore whether the implicit decomposition idea generalizes to other forms of decomposition or other non-stationarity issues (e.g., piecewise trends, irregular seasonal patterns).\n-\n\nQUESTIONS:\nI would consider increasing my score if most of the following points are clearly addressed. Please also refer to the weaknesses section.\n\n- How sensitive is DecompNet’s performance to the choice of decomposition hyperparameters? For instance, the moving average window size (25) was fixed. Did you try other window lengths or methods like STL, and if so, how did they affect results?\n- In the fusion stage with the learnable factor $\\lambda$, do you allow the weights $W_s$ and $W_t$ of the expert models to update as well, or is only $\\lambda$ learned? How stable is this training process? Does $\\lambda$ reliably converge to a meaningful value (e.g. close to 1 or something data-dependent), and have you observed any instances where the fused model underperformed the explicit two-model ensemble?\n- Implicit decomposition is demonstrated with two components (trend and seasonal). How would the framework extend to decomposing into more than two components (say seasonal/trend/residual)? Would you envision introducing multiple fusion factors (e.g. $\\lambda_1, \\lambda_2$) to fuse three expert models, or some iterative fusion process?\n- In existing explicit decomposition approaches, researchers sometimes choose heterogeneous model types for seasonal vs trend components (anticipating different dynamics). Your approach instead uses the same base architecture for both, which has the huge benefit of enabling weight fusion. Did you experiment with using different architectures for the two expert models without fusion (just to see performance)? If not fused, would, say, a CNN for seasonality and an RNN for trend perform better, or did you find that using the same architecture with decoupled training already captures both aspects well?\n- Could you provide more details on the training overhead introduced by DecompNet? For example, how much longer (in terms of epochs or wall-clock time) does it take to train a model with DecompNet compared to training the base model normally?\n\nLIMITATIONS:\nThe authors do address some limitations of their work, albeit briefly in the appendix. They acknowledge that their implementation of implicit decomposition is currently tied to the classic moving-average decomposition with a fixed seasonal/trend split. They note that extending the framework to use more advanced decomposition techniques (such as STL or frequency-domain methods ) and to decompose time series into finer components (e.g., adding a residual/noise component beyond just trend and seasonal) could further improve performance.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}