HN-MVTS: HyperNetwork-based Multivariate Time Series Forecasting

The introduction of HN-MVTS marks a notable advancement in multivariate time series forecasting, a field challenged by complex temporal dependencies. Traditional neural network models often struggle with performance due to their reliance on channel-dependent structures. HN-MVTS integrates a hypernetwork that generates weights for forecasting networks, allowing for improved generalization and predictive accuracy without increasing inference time. Extensive experiments conducted on eight benchmark datasets demonstrate that this model typically enhances the performance of existing state-of-the-art models like DLinear, PatchTST, and TSMixer. The findings suggest that hypernetwork-driven parameterization could be a promising direction for future forecasting techniques, potentially transforming how time series data is analyzed and predicted.