Title: SGN: Shifted Window-Based Hierarchical Variable Grouping for Multivariate Time Series Classification
Venue: NeurIPS 2025
Accepted: yes
Keywords: Deep Learning

Multivariate Time Series Classification
Abstract: Multivariate time series (MTS) classification has attracted increasing attention across various domains. Existing methods either decompose MTS into separate univariate series, ignoring inter-variable dependencies, or jointly model all variables, which may lead to over-smoothing and loss of semantic structure. These limitations become particularly pronounced when dealing with complex and heterogeneous variable types.
To address these challenges, we propose SwinGroupNet (SGN), which explores a novel perspective for constructing variable interaction and temporal dependency.
Specifically, SGN processes multi-scale time series using (1)
Variable Group Embedding (VGE), which partitions variables into groups and performs independent group-wise embedding; (2) Multi-Scale Group Window Mixing (MGWM), which reconstructs variable interactions by modeling both intra-group and inter-group dependencies while extracting multi-scale temporal features; and (3) Periodic Window Shifting and Merging (PWSM), which exploits inherent periodic patterns to enable hierarchical temporal interaction and feature aggregation. Extensive experiments on diverse benchmark datasets from multiple domains demonstrate that SGN consistently achieves state-of-the-art performance, with an average improvement of 4.2% over existing methods. We release the source code at https://anonymous.4open.science/r/SGN.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes SwinGroupNet (SGN), a novel framework for multivariate time series (MTS) classification that addresses limitations of existing methods by integrating structured variable grouping and hierarchical temporal feature extraction. The model comprises three core modules: 1) Variable Group Embedding (VGE), which partitions variables into groups based on similarity to capture intra-group and inter-group dependencies; 2) Multi-Scale Group Window Mixing (MGWM), which extracts multi-scale temporal features via periodic window partitioning and convolutional operations; 3) Periodic Window Shifting and Merging (PWSM), which leverages sequence periodic patterns to enable hierarchical temporal interaction. Extensive experiments on diverse benchmarks show SGN achieves state-of-the-art performance, with an average 4.2% accuracy improvement over existing methods.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. This paper introduces a novel perspective transforming multivariate interactions into structured intra/inter-group relationships, effectively balancing fine-grained dependency capture and over-smoothing avoidance.  \n2. Experimental validation covers domains including healthcare and human activity recognition, demonstrating consistent superiority over baselines.\n\nWeaknesses:\n1. In the Variable Group Embedding module, BDC (Brownian Distance Covariance) is used to model dependencies between variables. However, referring to Equation (11), its essence still relies on Euclidean distance. Does this approach genuinely capture better non-linear dependencies? Is there any performance improvement compared to other methods? Additionally, when calculating dependencies between variables, do you use the entire time series of that variable?\n2. \"Considering the extensibility of periodic patterns, we further incorporate multiple scales by merging additional period windows corresponding to the remaining Top-K dominant frequencies.\" (page=5, lines 155-157) This statement mentions merging period windows corresponding to the remaining frequencies in Top-K. Could you elaborate in detail on how this merging process is implemented?\n3. In the \"Multi-Scale Group Window Extracting\" section (Lines 159–172), operations of intra-group and inter-group pointwise convolutions are not explained. Mathematical formulations are needed to clarify channel dimension transformations. \n4. While moving and merging periodic windows enhances the capture of cross-window dependencies, what specific offset value and window size after merging yield optimal results? Please provide a quantitative analysis of this. \n5. In Section 4.2, the authors selected 10 multivariate time series datasets from the UEA. Many research reports have shown the performance of all datasets. How does your work perform on all datasets? Additionally, the hyperparameter sensitivity analysis lacks an evaluation of the regularization parameter $\\beta$.  \n6. Notation inconsistency: $k_i$ in Equation (6) conflicts with $n_k$ in Line 699 of Appendix D, causing confusion. Notation should be unified.\n\nQUESTIONS:\n--- In the Variable Group Embedding module, BDC (Brownian Distance Covariance) is used to model dependencies between variables. However, referring to Equation (11), its essence still relies on Euclidean distance. Does this approach genuinely capture better non-linear dependencies? Is there any performance improvement compared to other methods? Additionally, when calculating dependencies between variables, do you use the entire time series of that variable?\n--- \"Considering the extensibility of periodic patterns, we further incorporate multiple scales by merging additional period windows corresponding to the remaining Top-K dominant frequencies.\" (page=5, lines 155-157) This statement mentions merging period windows corresponding to the remaining frequencies in Top-K. Could you elaborate in detail on how this merging process is implemented?\n--- While moving and merging periodic windows enhances the capture of cross-window dependencies, what specific offset value and window size after merging yield optimal results? \n--- In Section 4.2, the authors selected 10 multivariate time series datasets from the UEA. Many research reports have shown the performance of all datasets. How does your work perform on all datasets? Additionally, the hyperparameter sensitivity analysis lacks an evaluation of the regularization parameter $\\beta$.\n\nLIMITATIONS:\nThe applicability of SGN in other time series tasks such as forecasting and imputation remains to be further explored. Moreover, the use of intrinsic similarity to generate the assignment matrix introduces additional computational overhead.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 5, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-18"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes SwinGroupNet (SGN), a novel framework for multivariate time series classification, which integrates three key components: Variable Group Embedding (VGE), Multi-scale Group Window Mixing (MGWM), and Periodic Window Shifting and Merging (PWSM). By combining these modules, SGN effectively captures both intra-group and inter-group variable dependencies across multiple temporal scales from raw multivariate time series data. Extensive experiments on four medical multivariate time series datasets and ten benchmark datasets from the UEA archive demonstrate that SGN achieves state-of-the-art classification performance.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths：\n\n1. The proposed VGE, MGWM, and PWSM modules are thoughtfully designed to address key aspects of multivariate time series analysis, including variable-wise dependency modeling, multi-scale feature extraction, and frequency-domain periodicity—factors that are central to advancing current time series research.\n\n2. The figures and tables are clearly presented and enhance the interpretability of the proposed method.\n\n3. The related work section is well-organized and comprehensive, providing a clear overview of the field’s recent advancements.\n\nWeaknesses：\n\n1. The overall novelty of the proposed SGN appears incremental, as it primarily integrates ideas from existing works. For instance, the core concept of the VGE module has been previously summarized and discussed in [R1], and shares similarities with contributions from [R2, R3]. Likewise, the multi-scale mixing strategy in MGWM resembles the approach adopted in TimeMixer [R4].\n\n2. Although VGE, MGWM, and PWSM are individually designed to capture distinct characteristics of multivariate time series, the conceptual and functional relationships among them are not clearly articulated. In particular, MGWM already captures multi-scale temporal patterns, which often include periodic structures. It remains unclear how the additional periodic modeling via PWSM complements or interacts with MGWM, and what underlying mechanism ensures their synergy.\n\n3. The authors claim in the abstract that existing methods either ignore inter-variable dependencies or model all variables jointly. However, a number of prior works have explicitly addressed inter-variable dependency modeling in multivariate time series classification [R5, R6], which are not discussed or empirically compared in the paper.\n\n4. For multivariate time series classification, the UEA 30 dataset is a widely accepted benchmark [R7, R8]. The authors evaluate their method on only 10 selected datasets, raising concerns about potential cherry-picking and limited generalizability of the results.\n\n\n[R1] A Comprehensive Survey of Deep Learning for Multivariate Time Series Forecasting: A Channel Strategy Perspective. arXiv, 2025.\n\n[R2] Crossformer: Transformer utilizing cross-dimension dependency for multivariate time series forecasting. ICLR, 2023.\n\n[R3] From similarity to superiority: Channel clustering for time series forecasting. NeurIPS, 2024.\n\n[R4] Timemixer: Decomposable multiscale mixing for time series forecasting. ICLR, 2024.\n\n[R5] SVP-T: A shape-level variable-position transformer for multivariate time series classification. AAAI, 2023.\n\n[R6] Fully-Connected Spatial-Temporal Graph for Multivariate Time Series Data. AAAI, 2024.\n\n[R7] The great multivariate time series classification bake off: a review and experimental evaluation of recent algorithmic advances. DMKD, 2021.\n\n[R8] Shapeformer: Shapelet transformer for multivariate time series classification. KDD, 2024.\n\nQUESTIONS:\n1. The UEA archive does not provide predefined validation sets for its sub-datasets. However, in the provided source code (data_loader.py, lines 723–725), it appears that a validation set is being generated. Could the authors clarify the strategy used to create the validation sets for the UEA datasets? Additionally, the released code does not include experimental scripts for running on the UEA datasets—would the authors consider making them publicly available to ensure reproducibility?\n\n2. Regarding the results shown in Figure 4, were all baseline results (e.g., ROCKET, TimesNet) re-implemented and re-evaluated by the authors under the same experimental setup? If so, could the authors provide details on how the reproduction was performed, including any modifications or hyperparameter settings used?\n\n3. Given the experimental settings adopted in [R8, R9] on the full UEA 30 time series dataset, could the authors report the classification performance of SGN under the same setting, using the published results from [R8, R9] as baselines (without the need to re-run those methods if time and resources are limited)? Such a comparison would provide a clearer understanding of SGN’s effectiveness relative to recent state-of-the-art approaches.\n\n4. The weaknesses outlined above—including concerns about incremental novelty, unclear interactions among proposed modules, limited UEA dataset coverage, and insufficient comparison with recent related methods—require further clarification and justification.\n\n\n[R9] MPTSNet: Integrating multiscale periodic local patterns and global dependencies for multivariate time series classification. AAAI, 2025.\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-29"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes SwinGroupNet (SGN), a novel approach for multivariate time series (MTS) classification, with key innovations including: Variable Group Embedding (VGE): A dynamic grouping strategy based on Brownian Distance Covariance (BDC) that partitions variables into clusters, enabling separate modeling of intra-group and inter-group dependencies to balance the trade-offs between independent and mixed variable modeling paradigms. Integrated periodic analysis and multi-scale convolution for simultaneous feature extraction across both temporal and variable dimensions. Periodic window shifting to enhance cross-window interactions and mitigate limitations of localized modeling.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. It demonstrates relatively excellent performance in multivariate time series classification tasks.\n\nWeaknesses:\n1. The architecture presented in Figure 2 is difficult to interpret. especially for the subfigures. \n2. Line 142.  the final loss includes the L_task, in fact, the experimental validation appears limited to classification tasks.\n\nQUESTIONS:\nThe proposed PWSM module exhibits strong dependence on periodic assumptions through its FFT-based windowing mechanism. This architectural choice may degrade model performance when processing non-periodic signals (e.g., white noise), (b) transient events (e.g., fault spikes in industrial data), or (c) irregular sampling scenarios - all common in real-world time series applications. What considerations did the author have regarding this?\n\nLIMITATIONS:\n1.Regarding the selection of baseline methods, we recommend incorporating state-of-the-art Multivariate Time Series representation approaches.\n2. Evaluating the method's generalizability through additional experiments on fundamental time-series tasks such as forecasting and anomaly detection.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper introduces SwinGroupNet (SGN), a novel framework for multivariate time series (MTS) classification that explicitly models both intra- and inter-variable dependencies using a grouped embedding approach and hierarchical temporal feature extraction. By combining three components—Variable Group Embedding (VGE), Multi-Scale Group Window Mixing (MGWM), and Periodic Window Shifting and Merging (PWSM)—SGN aims to preserve semantic structures, address heterogeneous variable types, and exploit periodic temporal patterns. Experimental results on multiple benchmark datasets show promising improvements over prior work.\n\nSTRENGTHS AND WEAKNESSES:\n# Strengths \n\n1. **Clear Motivation**: The paper convincingly discusses the limitations of univariate decomposition and full multivariate modeling approaches in MTS.\n2. **Novel Architecture**: The use of group-based embeddings and hierarchical temporal modeling (through PWSM and MGWM) is an interesting and original idea.\n3. **Strong empirical results**: Demonstrates consistent state-of-the-art performance across diverse benchmarks with a reported 4.2% improvement on average.\n4. **Code Availability**: Open-sourcing the implementation increases reproducibility and community impact.\n \n# Weaknesses\n- **Architectural Complexity Without Computational Justification**\n    \n    While SGN demonstrates strong performance, the proposed design—incorporating Variable Group Embedding (VGE), Multi-Scale Group Window Mixing (MGWM), and Periodic Window Shifting and Merging (PWSM)—introduces considerable architectural complexity. However, the paper **does not provide any analysis of computational cost** (e.g., training/inference time, parameter count, FLOPs, or GPU memory usage), which is critical when evaluating practical deployment, especially for long multivariate sequences. Without this, it is difficult to assess the **efficiency vs. performance tradeoff** compared to simpler or more lightweight baselines.\n    \n- **Lack of Controlled Comparison With Existing Channel Dependency Methods**\n    \n    Although the motivation for capturing inter-variable dependency is valid, the paper does not thoroughly compare SGN with **alternative methods for modeling channel correlations**. For instance, prior work like MSGNet (AAAI 2024) has explored efficient multi-scale inter-series correlation modeling. Additionally, **no study is conducted using a common architectural backbone** to test whether simpler methods (e.g., channel attention, global attention, temporal convolutions) underperform consistently compared to SGN.\n    \n- **Scalability**: No discussion is included on the computational complexity, especially for large MTS with many variables and long sequences.\n\nQUESTIONS:\n1- How does the computational cost (in terms of FLOPs, training time, inference time, and memory usage) of SGN compare to the baseline models?\n2- Does the model generalize well to datasets with fewer variables or less clear periodic structure?\nThe reliance on periodic patterns in PWSM might not apply broadly.\n\nLIMITATIONS:\nno", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-08"}