# Hierarchical Attention-Based Recurrent Highway Networks for Time Series Prediction

arXiv: https://arxiv.org/abs/1806.00685 · HTML: https://mlsanity.com/paper/1806.00685/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jun 2018.

## Built on by (most cited first, 3 of 3)

- [Granger Causality: A Review and Recent Advances](https://mlsanity.com/paper/2105.02675/) (2021)
- [Granger Causality using Neural Networks](https://mlsanity.com/paper/2208.03703/) (2022)
- [Multi-Step Short-Term Wind Speed Prediction with Rank Pooling and Fast Fourier Transformation](https://mlsanity.com/paper/2211.14434/) (2022)

## Abstract (arXiv)

Time series prediction has been studied in a variety of domains. However, it is still challenging to predict future series given historical observations and past exogenous data. Existing methods either fail to consider the interactions among different components of exogenous variables which may affect the prediction accuracy, or cannot model the correlations between exogenous data and target data. Besides, the inherent temporal dynamics of exogenous data are also related to the target series prediction, and thus should be considered as well. To address these issues, we propose an end-to-end deep learning model, i.e., Hierarchical attention-based Recurrent Highway Network (HRHN), which incorporates spatio-temporal feature extraction of exogenous variables and temporal dynamics modeling of target variables into a single framework. Moreover, by introducing the hierarchical attention mechanism, HRHN can adaptively select the relevant exogenous features in different semantic levels. We carry out comprehensive empirical evaluations with various methods over several datasets, and show that HRHN outperforms the state of the arts in time series prediction, especially in capturing sudden changes and sudden oscillations of time series.