Sonnet: Spectral Operator Neural Network for Multivariable Time Series Forecasting

• URL: http://arxiv.org/abs/2505.15312v1
• Date: Wed, 21 May 2025 09:43:12 GMT
• Title: Sonnet: Spectral Operator Neural Network for Multivariable Time Series Forecasting
• Authors: Yuxuan Shu, Vasileios Lampos,
• Abstract summary: We propose a novel architecture, namely the Spectral Operator Neural Network (Sonnet)<n>Sonnet applies learnable wavelet transformations to the input and incorporates spectral analysis using the Koopman operator.<n>Our empirical analysis shows that Sonnet yields the best performance on $34$ out of $47$ forecasting tasks.
• Score: 0.34530027457862006
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multivariable time series forecasting methods can integrate information from exogenous variables, leading to significant prediction accuracy gains. Transformer architecture has been widely applied in various time series forecasting models due to its ability to capture long-range sequential dependencies. However, a na\"ive application of transformers often struggles to effectively model complex relationships among variables over time. To mitigate against this, we propose a novel architecture, namely the Spectral Operator Neural Network (Sonnet). Sonnet applies learnable wavelet transformations to the input and incorporates spectral analysis using the Koopman operator. Its predictive skill relies on the Multivariable Coherence Attention (MVCA), an operation that leverages spectral coherence to model variable dependencies. Our empirical analysis shows that Sonnet yields the best performance on $34$ out of $47$ forecasting tasks with an average mean absolute error (MAE) reduction of $1.1\%$ against the most competitive baseline (different per task). We further show that MVCA -- when put in place of the na\"ive attention used in various deep learning models -- can remedy its deficiencies, reducing MAE by $10.7\%$ on average in the most challenging forecasting tasks.

Related papers

• vLinear: A Powerful Linear Model for Multivariate Time Series Forecasting [28.587343014443576]
  vecTrans is a lightweight module that utilizes a learnable vector to model multivariate correlations.<n>WFMLoss is an effective plug-and-play objective, consistently improving existing forecasters.
  arXiv  Detail & Related papers  (2026-01-20T09:23:10Z)
• A Unified Frequency Domain Decomposition Framework for Interpretable and Robust Time Series Forecasting [81.73338008264115]
  Current approaches for time series forecasting, whether in the time or frequency domain, predominantly use deep learning models based on linear layers or transformers.<n>We propose FIRE, a unified frequency domain decomposition framework that provides a mathematical abstraction for diverse types of time series.<n>Fire consistently outperforms state-of-the-art models on long-term forecasting benchmarks.
  arXiv  Detail & Related papers  (2025-10-11T09:59:25Z)
• A Wireless Foundation Model for Multi-Task Prediction [50.21098141769079]
  We propose a unified foundation model for multi-task prediction in wireless networks that supports diverse prediction intervals.<n>After trained on large-scale datasets, the proposed foundation model demonstrates strong generalization to unseen scenarios and zero-shot performance on new tasks.
  arXiv  Detail & Related papers  (2025-07-08T12:37:55Z)
• MFRS: A Multi-Frequency Reference Series Approach to Scalable and Accurate Time-Series Forecasting [51.94256702463408]
  Time series predictability is derived from periodic characteristics at different frequencies.<n>We propose a novel time series forecasting method based on multi-frequency reference series correlation analysis.<n> Experiments on major open and synthetic datasets show state-of-the-art performance.
  arXiv  Detail & Related papers  (2025-03-11T11:40:14Z)
• EDformer: Embedded Decomposition Transformer for Interpretable Multivariate Time Series Predictions [4.075971633195745]
  This paper introduces an embedded transformer, 'EDformer', for time series forecasting tasks.<n>Without altering the fundamental elements, we reuse the Transformer architecture and consider the capable functions of its constituent parts.<n>The model obtains state-of-the-art predicting results in terms of accuracy and efficiency on complex real-world time series datasets.
  arXiv  Detail & Related papers  (2024-12-16T11:13:57Z)
• Timer-XL: Long-Context Transformers for Unified Time Series Forecasting [67.83502953961505]
  We present Timer-XL, a causal Transformer for unified time series forecasting.<n>Based on large-scale pre-training, Timer-XL achieves state-of-the-art zero-shot performance.
  arXiv  Detail & Related papers  (2024-10-07T07:27:39Z)
• MGCP: A Multi-Grained Correlation based Prediction Network for Multivariate Time Series [54.91026286579748]
  We propose a Multi-Grained Correlations-based Prediction Network. It simultaneously considers correlations at three levels to enhance prediction performance. It employs adversarial training with an attention mechanism-based predictor and conditional discriminator to optimize prediction results at coarse-grained level.
  arXiv  Detail & Related papers  (2024-05-30T03:32:44Z)
• TSLANet: Rethinking Transformers for Time Series Representation Learning [19.795353886621715]
  Time series data is characterized by its intrinsic long and short-range dependencies. We introduce a novel Time Series Lightweight Network (TSLANet) as a universal convolutional model for diverse time series tasks. Our experiments demonstrate that TSLANet outperforms state-of-the-art models in various tasks spanning classification, forecasting, and anomaly detection.
  arXiv  Detail & Related papers  (2024-04-12T13:41:29Z)
• Parsimony or Capability? Decomposition Delivers Both in Long-term Time Series Forecasting [46.63798583414426]
  Long-term time series forecasting (LTSF) represents a critical frontier in time series analysis. Our study demonstrates, through both analytical and empirical evidence, that decomposition is key to containing excessive model inflation. Remarkably, by tailoring decomposition to the intrinsic dynamics of time series data, our proposed model outperforms existing benchmarks.
  arXiv  Detail & Related papers  (2024-01-22T13:15:40Z)
• CARD: Channel Aligned Robust Blend Transformer for Time Series Forecasting [50.23240107430597]
  We design a special Transformer, i.e., Channel Aligned Robust Blend Transformer (CARD for short), that addresses key shortcomings of CI type Transformer in time series forecasting. First, CARD introduces a channel-aligned attention structure that allows it to capture both temporal correlations among signals. Second, in order to efficiently utilize the multi-scale knowledge, we design a token blend module to generate tokens with different resolutions. Third, we introduce a robust loss function for time series forecasting to alleviate the potential overfitting issue.
  arXiv  Detail & Related papers  (2023-05-20T05:16:31Z)
• SpectraNet: Multivariate Forecasting and Imputation under Distribution Shifts and Missing Data [40.21502451136054]
  SpectraNet is a novel time-series forecasting model that dynamically infers temporal dynamics and correlations on the recent observed history. A Convolution Neural Network maps the learned representation by sequentially mixing its components and refining the output. Our proposed approach can simultaneously produce forecasts and interpolate past observations and can, therefore, greatly simplify production systems.
  arXiv  Detail & Related papers  (2022-10-22T18:07:31Z)
• Are Transformers Effective for Time Series Forecasting? [13.268196448051308]
  Recently, there has been a surge of Transformer-based solutions for the time series forecasting (TSF) task. This study investigates whether Transformer-based techniques are the right solutions for long-term time series forecasting. We find that the relatively higher long-term forecasting accuracy of Transformer-based solutions has little to do with the temporal relation extraction capabilities of the Transformer architecture.
  arXiv  Detail & Related papers  (2022-05-26T17:17:08Z)
• 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)

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.