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Related Experiment Video

Updated: Sep 7, 2025

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
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Hierarchical attention network for multivariate time series long-term forecasting.

Hongjing Bi1, Lilei Lu1, Yizhen Meng1

  • 1Department of Computer Science, Tangshan Normal University, Tangshan, Hebei 063000 People's Republic of China.

Applied Intelligence (Dordrecht, Netherlands)
|June 22, 2022
PubMed
Summary

This study introduces a Hierarchical Attention Network (HANet) for multivariate time series long-term forecasting. HANet effectively addresses irrelevant factors and improves prediction accuracy by using factor-aware and multi-modal fusion networks.

Keywords:
Deep neural networkHierarchical attentionLong-term forecastingMulti-modal fusionMultivariate time series

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Area of Science:

  • Data Science
  • Machine Learning
  • Artificial Intelligence

Background:

  • Multivariate time series long-term forecasting is crucial in economics, finance, and traffic analysis.
  • Existing attention-based Recurrent Neural Networks (RNNs) struggle with irrelevant factors and factor conflicts.

Purpose of the Study:

  • To propose a novel Hierarchical Attention Network (HANet) for improved multivariate time series long-term forecasting.
  • To address the limitations of current models in handling irrelevant exogenous factors and their conflicts with target factors.

Main Methods:

  • HANet incorporates a factor-aware attention network (FAN) to minimize the impact of irrelevant factors.
  • A multi-modal fusion network (MFN) with a fusion gate adaptively integrates information from target and exogenous factors.

Main Results:

  • HANet demonstrates superior performance compared to state-of-the-art methods on real-world datasets.
  • The proposed model offers interpretability in its predictions.

Conclusions:

  • HANet provides an effective solution for multivariate time series long-term forecasting.
  • The model's ability to handle complex factor interactions enhances prediction accuracy and interpretability.