IEEE Access, 10:11871–11885, 2022. Publisher: Institute of Electrical and Electronics Engineers (\IEEE\)
![Recurrent Neural Network-Augmented Locally Adaptive Interpretable Regression for Multivariate Time-Series Forecasting [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex
Paper doi abstract bibtex Explaining dynamic relationships between input and output variables is one of the most important issues in time dependent domains such as economic, finance and so on. In this work, we propose a novel locally adaptive interpretable deep learning architecture that is augmented by recurrent neural networks to provide model explainability and high predictive accuracy for time-series data. The proposed model relies on two key aspects. First, the base model should be a simple interpretable model. In this step, we obtain our base model using a simple linear regression and statistical test. Second, we use recurrent neural networks to re-parameterize our base model to make the regression coefficients adaptable for each time step. Our experimental results on public benchmark datasets showed that our model not only achieves better predictive performance than the state-of-the-art baselines, but also discovers the dynamic relationship between input and output variables.
@article{Munkhdalai_2022,
title = {Recurrent {Neural} {Network}-{Augmented} {Locally} {Adaptive} {Interpretable} {Regression} for {Multivariate} {Time}-{Series} {Forecasting}},
volume = {10},
issn = {21693536},
url = {https://doi.org/10.1109%2Faccess.2022.3145951},
doi = {10.1109/ACCESS.2022.3145951},
abstract = {Explaining dynamic relationships between input and output variables is one of the most important issues in time dependent domains such as economic, finance and so on. In this work, we propose a novel locally adaptive interpretable deep learning architecture that is augmented by recurrent neural networks to provide model explainability and high predictive accuracy for time-series data. The proposed model relies on two key aspects. First, the base model should be a simple interpretable model. In this step, we obtain our base model using a simple linear regression and statistical test. Second, we use recurrent neural networks to re-parameterize our base model to make the regression coefficients adaptable for each time step. Our experimental results on public benchmark datasets showed that our model not only achieves better predictive performance than the state-of-the-art baselines, but also discovers the dynamic relationship between input and output variables.},
journal = {IEEE Access},
author = {Munkhdalai, Lkhagvadorj and Munkhdalai, Tsendsuren and Pham, Van Huy and Li, Meijing and Ryu, Keun Ho and Theera-Umpon, Nipon},
year = {2022},
note = {Publisher: Institute of Electrical and Electronics Engineers (\{IEEE\})},
keywords = {Explainable AI, linear regression, recurrent neural network, time-series forecasting},
pages = {11871--11885},
}Downloads: 0