HN-MVTS: HyperNetwork-based Multivariate Time Series Forecasting

2025-11-11

AI Summary

In multivariate time series (MVTS) forecasting, explicitly modeling channel dependencies often leads to overfitting and degraded performance, whereas lightweight, channel-independent models exhibit superior robustness. To address this, we propose HN-MVTS—a hypernetwork-based framework that dynamically generates the final-layer weights of a target model as a data-adaptive regularization term. This approach enhances generalization and long-horizon forecasting accuracy without increasing inference cost. Crucially, HN-MVTS only fine-tunes the output-layer parameters, ensuring seamless compatibility with mainstream architectures (e.g., DLinear, PatchTST) and enabling plug-and-play enhancement. Extensive experiments across eight benchmark datasets demonstrate that HN-MVTS consistently improves state-of-the-art models, with particularly pronounced gains in long-term forecasting—achieving statistically significant reductions in MAE and MSE over baseline methods.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Computer Vision: Large Vision Models
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Web Mining and Content Analysis: Large pretrained models with web data

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.

Problem

• Addresses multivariate time series forecasting challenges with complex dependencies
• Improves channel-dependent models' performance without increasing inference time
• Enhances generalization through hypernetwork-generated weights as adaptive regularizer

Innovation

• Hypernetwork generates final layer weights adaptively
• Learns component embeddings to capture channel dependencies
• Training-only hypernetwork boosts accuracy without inference overhead