IConv: Focusing on Local Variation with Channel Independent Convolution for Multivariate Time Series Forecasting

• URL: http://arxiv.org/abs/2509.20783v1
• Date: Thu, 25 Sep 2025 06:09:37 GMT
• Title: IConv: Focusing on Local Variation with Channel Independent Convolution for Multivariate Time Series Forecasting
• Authors: Gawon Lee, Hanbyeol Park, Minseop Kim, Dohee Kim, Hyerim Bae,
• Abstract summary: Real-world time-series data often exhibit non-stationarity, including changing trends, irregular seasonality, and residuals.<n>Recently proposed multi-layer perceptron (MLP)-based models have shown excellent performance to capture long-term dependency.<n>We propose IConv, a novel architecture that processes the temporal channel independently and considers the inter-channel relationship.
• Score: 6.27761817493579
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Real-world time-series data often exhibit non-stationarity, including changing trends, irregular seasonality, and residuals. In terms of changing trends, recently proposed multi-layer perceptron (MLP)-based models have shown excellent performance owing to their computational efficiency and ability to capture long-term dependency. However, the linear nature of MLP architectures poses limitations when applied to channels with diverse distributions, resulting in local variations such as seasonal patterns and residual components being ignored. However, convolutional neural networks (CNNs) can effectively incorporate these variations. To resolve the limitations of MLP, we propose combining them with CNNs. The overall trend is modeled using an MLP to consider long-term dependencies. The CNN uses diverse kernels to model fine-grained local patterns in conjunction with MLP trend predictions. To focus on modeling local variation, we propose IConv, a novel convolutional architecture that processes the temporal dependency channel independently and considers the inter-channel relationship through distinct layers. Independent channel processing enables the modeling of diverse local temporal dependencies and the adoption of a large kernel size. Distinct inter-channel considerations reduce computational cost. The proposed model is evaluated through extensive experiments on time-series datasets. The results reveal the superiority of the proposed method for multivariate time-series forecasting.

Related papers

• MS-TVNet:A Long-Term Time Series Prediction Method Based on Multi-Scale Dynamic Convolution [6.712251433139411]
  We propose MS-TVNet, a multi-scale 3D dynamic convolutional neural network.<n>Our findings highlight the effectiveness of leveraging convolutional networks for capturing complex temporal patterns.
  arXiv  Detail & Related papers  (2025-06-08T10:33:39Z)
• TimeCF: A TimeMixer-Based Model with adaptive Convolution and Sharpness-Aware Minimization Frequency Domain Loss for long-term time seris forecasting [5.032613143415414]
  We propose a deep learning model TimeCF for long-term time series forecasting based on the TimeMixer.<n>TimeCF decomposes the original time series into sequences of different scales.<n>Different scales are aggregated through a Feed-Forward Network.
  arXiv  Detail & Related papers  (2025-05-23T06:39:20Z)
• CMamba: Channel Correlation Enhanced State Space Models for Multivariate Time Series Forecasting [18.50360049235537]
  Mamba, a state space model, has emerged with robust sequence and feature mixing capabilities. Capturing cross-channel dependencies is critical in enhancing performance of time series prediction. We introduce a refined Mamba variant tailored for time series forecasting.
  arXiv  Detail & Related papers  (2024-06-08T01:32:44Z)
• Adaptive Multi-Scale Decomposition Framework for Time Series Forecasting [26.141054975797868]
  We propose a novel Adaptive Multi-Scale Decomposition (AMD) framework for time series forecasting.<n>Our framework decomposes time series into distinct temporal patterns at multiple scales, leveraging the Multi-Scale Decomposable Mixing (MDM) block.<n>Our approach effectively models both temporal and channel dependencies and utilizes autocorrelation to refine multi-scale data integration.
  arXiv  Detail & Related papers  (2024-06-06T05:27:33Z)
• SOFTS: Efficient Multivariate Time Series Forecasting with Series-Core Fusion [59.96233305733875]
  Time series forecasting plays a crucial role in various fields such as finance, traffic management, energy, and healthcare. Several methods utilize mechanisms like attention or mixer to address this by capturing channel correlations. This paper presents an efficient-based model, the Series-cOre Fused Time Series forecaster (SOFTS)
  arXiv  Detail & Related papers  (2024-04-22T14:06:35Z)
• PDETime: Rethinking Long-Term Multivariate Time Series Forecasting from the perspective of partial differential equations [49.80959046861793]
  We present PDETime, a novel LMTF model inspired by the principles of Neural PDE solvers. Our experimentation across seven diversetemporal real-world LMTF datasets reveals that PDETime adapts effectively to the intrinsic nature of the data.
  arXiv  Detail & Related papers  (2024-02-25T17:39:44Z)
• FCDNet: Frequency-Guided Complementary Dependency Modeling for Multivariate Time-Series Forecasting [9.083469629116784]
  We propose FCDNet, a concise yet effective framework for time-series forecasting. It helps extract long- and short-term dependency information adaptively from multi-level frequency patterns. Experiments show that FCDNet significantly exceeds strong baselines.
  arXiv  Detail & Related papers  (2023-12-27T07:29:52Z)
• The Capacity and Robustness Trade-off: Revisiting the Channel Independent Strategy for Multivariate Time Series Forecasting [50.48888534815361]
  We show that models trained with the Channel Independent (CI) strategy outperform those trained with the Channel Dependent (CD) strategy. Our results conclude that the CD approach has higher capacity but often lacks robustness to accurately predict distributionally drifted time series. We propose a modified CD method called Predict Residuals with Regularization (PRReg) that can surpass the CI strategy.
  arXiv  Detail & Related papers  (2023-04-11T13:15:33Z)
• Multi-scale Attention Flow for Probabilistic Time Series Forecasting [68.20798558048678]
  We propose a novel non-autoregressive deep learning model, called Multi-scale Attention Normalizing Flow(MANF) Our model avoids the influence of cumulative error and does not increase the time complexity. Our model achieves state-of-the-art performance on many popular multivariate datasets.
  arXiv  Detail & Related papers  (2022-05-16T07:53:42Z)
• TACTiS: Transformer-Attentional Copulas for Time Series [76.71406465526454]
  estimation of time-varying quantities is a fundamental component of decision making in fields such as healthcare and finance. We propose a versatile method that estimates joint distributions using an attention-based decoder. We show that our model produces state-of-the-art predictions on several real-world datasets.
  arXiv  Detail & Related papers  (2022-02-07T21:37:29Z)
• Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks [91.65637773358347]
  We propose a general graph neural network framework designed specifically for multivariate time series data. Our approach automatically extracts the uni-directed relations among variables through a graph learning module. Our proposed model outperforms the state-of-the-art baseline methods on 3 of 4 benchmark datasets.
  arXiv  Detail & Related papers  (2020-05-24T04:02:18Z)
• Transformer Hawkes Process [79.16290557505211]
  We propose a Transformer Hawkes Process (THP) model, which leverages the self-attention mechanism to capture long-term dependencies. THP outperforms existing models in terms of both likelihood and event prediction accuracy by a notable margin. We provide a concrete example, where THP achieves improved prediction performance for learning multiple point processes when incorporating their relational information.
  arXiv  Detail & Related papers  (2020-02-21T13:48:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.