HN-MVTS: HyperNetwork-based Multivariate Time Series Forecasting

TL;DR

This paper introduces HN-MVTS, a hypernetwork-based approach that enhances multivariate time series forecasting models by improving their generalization and accuracy without increasing inference time.

Contribution

The paper proposes a novel hypernetwork architecture that generates the last layer weights of forecasting models, serving as a data-adaptive regularizer to improve performance.

Findings

Improves performance of state-of-the-art models on benchmark datasets.

Enhances long-range predictive accuracy.

Does not increase inference time.

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.