Title: Time-o1: Time-Series Forecasting Needs Transformed Label Alignment
Venue: NeurIPS 2025
Accepted: yes
Keywords: Time-Series

Label Autocorrelation

Orthogonalization
Abstract: Training time-series forecasting models poses unique challenges in loss function design. Most existing approaches adopt temporal mean squared error, but this study reveals two critical limitations: (1) it ignores the presence of label autocorrelation, which biases it from the true label sequence likelihood;  (2) it involves excessive number of tasks, which complicates optimization, especially for long-term forecasting. To address these issues, we introduce Time-o1, a transform-enhanced loss function for time-series forecasting. The central idea is to transform the label sequence into decorrelated components with discriminated significance. Models are then trained to align the most significant components, thereby effectively mitigating label autocorrelation and reducing task amount. Experiments demonstrate that Time-o1 achieves state-of-the-art performance and is compatible with various forecast models. Code is available at https://github.com/Master-PLC/Time-o1.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nExisting methods predominantly utilize the temporal mean squared error, which faces two critical challenges: (1) label autocorrelation, which leads to bias from the label sequence likelihood; (2) excessive amount of tasks, which increases with the forecast horizon and complicates optimization. In this work, the authors propose Transform-enhanced Direct Forecast (TransDF), which transforms the label sequence into decorrelated components with discriminated significance. Models are trained to align the most significant components, thereby effectively mitigating label autocorrelation and reducing task amount.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. The core idea of applying PCA-like transformations to the label space to mitigate autocorrelation bias and reduce task complexity is novel and impactful. The use of SVD to generate decorrelated components ranked by significance provides a theoretically grounded solution. This approach effectively bridges a gap in objective design, which has been underexplored compared to architectural advancements.\n2. The paper validates TransDF rigorously across 8+ datasets, 11+ SOTA baselines, and multiple forecasting models (Transformers, MLPs, etc.). Ablation studies clearly dissect the contributions of decorrelation and task reduction, while sensitivity analyses justify design choices. The consistent improvements (e.g., Fredformer + TransDF reduces MSE by 0.016 on ETTh1) demonstrate broad applicability.\n\nWeaknesses:\n1. While Theorem 3.1 states the bias of TMSE under autocorrelation, its proof is relegated to Appendix without intuitive explanation in the main text. The connection between SVD-based decorrelation (Lemma 3.2) and bias elimination (§3.2) lacks formal derivation. A corollary linking Lemma 3.2 to Theorem 3.1’s bias term would strengthen theoretical rigor.\n2. TransDF introduces SVD on label sequences during training. While §E.1 (Appendix) mentions efficiency, the main text does not quantify its cost relative to forecast horizon T or compare it to FreDF/Fourier transforms. For long horizons, this could be non-trivial. A complexity analysis for SVD) and wall-clock time comparisons would clarify practical trade-offs.\n\nQUESTIONS:\nPlease see Weaknesses!\n\nLIMITATIONS:\nPlease see Weaknesses!", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-21"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a new label transformation method that transforms time series forecasting labels to decorrelate labels and mitigate the unimportant forecasting labels, so as to reduce forecasting tasks. The proposed method can then enhance the performance of forecasting models and maintain high efficiency. Experiment results show that the proposed method can help improve the performance of existing forecasting models as a plug-and-play module. Comprehensive results further prove the effectiveness of the label decorrelation and reduction of the number of tasks.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. The paper is well written with comprehensive details and results.\n2. The idea is well motivated and explained. The motivation examples are easy to follow and make sense.\n3. The proposed method is efficient but effective. Experiments are designed carefully to show the superiority of the proposed method and to research various aspects of the proposed method.\n4. The provided code is comprehensive and structured clearly for researchers to follow.\n\nWeaknesses:\n1. Some conclusions are very precise. For example, the paper claims, \"We observe that increasing $\\alpha$ from 0 to 1 generally leads to improved forecasting accuracy, with the best results typically achieved when $\\alpha$ is close to 1.\" However, from the results, the best results occur at $\\alpha=1$ for ETTh2, ETTm1, and ETTm2. This should be presented precisely and explained clearly in the paper.\n2. The parameter sensitivity on different tasks shows different patterns, with some datasets showing a decrease with $\\alpha$ increases, and on other datasets showing a first decrease and sharp increase at $\\alpha=1$. These inconsistent patterns indicate potential failure of unexpected behaviours in some cases.\n3. To demonstrate the effectiveness of the method as a plug-and-play module, it would be better to see whether the proposed method improves forecasting performance on more base forecasting models, like non-deep learning ones, instead of four pure deep learning forecasting models.\n4. The paper does not really discuss limitations, although it has a section called \"limitations and future works\".\n\nQUESTIONS:\nHow to interpret the sharp error increase at $\\alpha=1$ for some datasets like weather, while other datasets do not?\n\nHow does the proposed model perform when applied to non-deep learning forecasting models?\n\nLIMITATIONS:\nThe paper discusses future work, but does not really discuss limitations, even though it has a \"limitations and future works\" section.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-01"}

comments: SUMMARY:
TransDF proposes a novel training objective that decorrelates the label space and aligns the most informative components to mitigate label autocorrelation and reduce the task amount. It can be plugged into existing time-series forecasting (TSF) models by reformulating the loss objective and applying an SVD-based label transformation.

STRENGTHS AND WEAKNESSES:
Strengths
- The paper clearly formulates the bias issue of the TMSE objective in Theorem 3.1 and addresses it by aligning fully decorrelated label components through SVD-based transformation.
- While FreDF relies on the assumption of decorrelation between frequency bases,which only holds under infinite forecast horizons, TransDF removes label autocorrelation bias under realistic finite horizons and simultaneously reduces the number of forecasting tasks.

Weaknesses
- The proposed method applies a global projection matrix derived from SVD uniformly across all forecast steps. However, real-world time-series data often contain local pattern shifts or nonlinear/dynamic structures. Thus, a static global projection may fail to capture such local dynamics, which might explain smaller improvements on certain datasets (e.g., PEMS or Traffic).

QUESTIONS:
Why does even TransDF† (which uses a random projection and only partial component alignment) outperform the canonical DF objective, especially on the Weather dataset?

LIMITATIONS:
The SVD-based label transformation relies solely on label-side prior statistics, making it less suitable for fully online or streaming scenarios. This limitation may hinder deployment in real-time TSF systems, where adaptive behavior and incremental updates are often required.
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 2
DATE: 2025-07-03

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper tackles a fundamental but underexplored aspect of neural time‑series forecasting, i.e., the choice of learning objective. Building on the standard direct‑forecast paradigm (which minimizes pointwise temporal MSE), the authors identify two critical shortcomings of this objective in the forecasting setting, Label autocorrelation bias and Excessive multitask complexity. To address these, the authors propose TransDF, which can be dropped in to any existing architecture without structural changes. Extensive experiments across multiple benchmark datasets (ETT, ECL, Weather, M4) and forecasting models (Transformers, MLPs, GNNs) demonstrate that TransDF outperforms both the canonical TMSE loss and a variety of shape‑alignment or frequency‑domain objectives, achieving new state‑of‑the‑art results. An ablation study further isolates the benefits of decorrelation versus task reduction, confirming that their combination is key to TransDF’s success .\n\nSTRENGTHS AND WEAKNESSES:\n## Strengths:\n1. The proposed transformation is grounded in sound linear‑algebraic principles, which directly targets the source of bias and task redundancy.\n2. The method is model‑agnostic and “drop‑in”—it requires no architectural changes and gracefully extends to any direct‑forecasting network, as evidenced by consistent improvements on FredFormer, iTransformer, FreTS, DLinear, etc.\n3. TransDF’s randomized, data‑adapted projections guarantee full decorrelation at finite horizons and discriminate component importance, which is novel to me.\n\n## Weaknesses:\n1. The paper could more deeply explore or theoretically characterize when and why certain projection strategies might outperform the randomized approach.\n2. The Limitations section is concise but could better discuss potential failure modes, e.g., cases where label autocorrelation structure is nonstationary or when small-variance components carry critical signals (anomaly forecasting).\n3. The core idea of transforming labels into another basis has precedents in frequency‑domain losses (FreDF) and shape‑alignment objectives; the manuscript could more explicitly delineate how TransDF’s theoretical guarantees and practical gains substantially surpass these earlier approaches beyond empirical metrics.\n\nQUESTIONS:\nSee Weaknesses.\n\nLIMITATIONS:\nThe Limitations section is concise but could better discuss potential failure modes, e.g., cases where label autocorrelation structure is nonstationary or when small-variance components carry critical signals (anomaly forecasting).", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-03"}