# Explainable Parallel RCNN with Novel Feature Representation for Time Series Forecasting

arXiv: https://arxiv.org/abs/2305.04876 · HTML: https://mlsanity.com/paper/2305.04876/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 36 references that are in the map.
- First posted on arXiv 8 May 2023.

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Visualizing the Loss Landscape of Neural Nets](https://mlsanity.com/paper/1712.09913/) (2017)
- [DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks](https://mlsanity.com/paper/1704.04110/) (2017)
- [Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting](https://mlsanity.com/paper/1912.09363/) (2019)
- [Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting](https://mlsanity.com/paper/1907.00235/) (2019)
- [Professor Forcing: A New Algorithm for Training Recurrent Networks](https://mlsanity.com/paper/1610.09038/) (2016)
- [A Multi-Horizon Quantile Recurrent Forecaster](https://mlsanity.com/paper/1711.11053/) (2017)
- [A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Data](https://mlsanity.com/paper/1811.08055/) (2018)
- [Multi-task Sequence to Sequence Learning](https://mlsanity.com/paper/1511.06114/) (2015)
- [Probabilistic Forecasting with Temporal Convolutional Neural Network](https://mlsanity.com/paper/1906.04397/) (2019)
- [XCM: An Explainable Convolutional Neural Network for Multivariate Time Series Classification](https://mlsanity.com/paper/2009.04796/) (2020)
- [Position-based Content Attention for Time Series Forecasting with Sequence-to-sequence RNNs](https://mlsanity.com/paper/1703.10089/) (2017)
- [Time Series Forecasting (TSF) Using Various Deep Learning Models](https://mlsanity.com/paper/2204.11115/) (2022)

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

- [Deep Learning Models for Flood Predictions in South Florida](https://mlsanity.com/paper/2306.15907/) (2023)
- [The Power of Explainability in Forecast-Informed Deep Learning Models for Flood Mitigation](https://mlsanity.com/paper/2310.19166/) (2023)
- [Graph Transformer Network for Flood Forecasting with Heterogeneous Covariates](https://mlsanity.com/paper/2310.07631/) (2023)

## Abstract (arXiv)

Accurate time series forecasting is a fundamental challenge in data science. It is often affected by external covariates such as weather or human intervention, which in many applications, may be predicted with reasonable accuracy. We refer to them as predicted future covariates. However, existing methods that attempt to predict time series in an iterative manner with autoregressive models end up with exponential error accumulations. Other strategies hat consider the past and future in the encoder and decoder respectively limit themselves by dealing with the historical and future data separately. To address these limitations, a novel feature representation strategy -- shifting -- is proposed to fuse the past data and future covariates such that their interactions can be considered. To extract complex dynamics in time series, we develop a parallel deep learning framework composed of RNN and CNN, both of which are used hierarchically. We also utilize the skip connection technique to improve the model's performance. Extensive experiments on three datasets reveal the effectiveness of our method. Finally, we demonstrate the model interpretability using the Grad-CAM algorithm.