arXiv:2511.08340v2 Announce Type: replace 
Abstract: Accurate forecasting of multivariate time series data remains a formidable challenge, particularly due to the growing complexity of temporal dependencies in real-world scenarios. While neural network-based models have achieved notable success in this domain, complex channel-dependent models often suffer from performance degradation compared to channel-independent models that do not consider the relationship between components but provide high robustness due to small capacity. In this work, we propose HN-MVTS, a novel architecture that integrates a hypernetwork-based generative prior with an arbitrary neural network forecasting model. The input of this hypernetwork is a learnable embedding matrix of time series components. To restrict the number of new parameters, the hypernetwork learns to generate the weights of the last layer of the target forecasting networks, serving as a data-adaptive regularizer that improves generalization and long-range predictive accuracy. The hypernetwork is used only during the training, so it does not increase the inference time compared to the base forecasting model. Extensive experiments on eight benchmark datasets demonstrate that application of HN-MVTS to the state-of-the-art models (DLinear, PatchTST, TSMixer, etc.) typically improves their performance. Our findings suggest that hypernetwork-driven parameterization offers a promising direction for enhancing existing forecasting techniques in complex scenarios.

تم اقتراح بنية جديدة تُدعى HN-MVTS للتنبؤ بالسلاسل الزمنية المتعددة المتغيرات، والتي تدمج أولوية توليدية قائمة على الشبكات الفائقة مع نموذج شبكة عصبية. تهدف هذه الطريقة إلى تحسين دقة التنبؤ من خلال معالجة تعقيدات الاعتماد الزمني في البيانات المتعددة المتغيرات، والتي غالبًا ما تكافح النماذج التقليدية لإدارتها بشكل فعال.

Se ha propuesto una nueva arquitectura llamada HN-MVTS para la predicción de series temporales multivariadas, que integra un prior generativo basado en un hipernetwork con un modelo de red neuronal. Este enfoque busca mejorar la precisión de las predicciones al abordar las complejidades de las dependencias temporales en datos multivariados, que los modelos tradicionales a menudo tienen dificultades para gestionar de manera efectiva.

Une nouvelle architecture nommée HN-MVTS a été proposée pour la prévision de séries temporelles multivariées, intégrant un prior génératif basé sur un hyperréseau avec un modèle de réseau neuronal. Cette approche vise à améliorer la précision des prévisions en abordant les complexités des dépendances temporelles dans les données multivariées, que les modèles traditionnels ont souvent du mal à gérer efficacement.

A novel architecture named HN-MVTS has been proposed for multivariate time series forecasting, integrating a hypernetwork-based generative prior with a neural network model. This approach aims to enhance the accuracy of forecasting by addressing the complexities of temporal dependencies in multivariate data, which traditional models often struggle to manage effectively.

HN-MVTS: HyperNetwork-based Multivariate Time Series Forecasting

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