# Position-based Content Attention for Time Series Forecasting with Sequence-to-sequence RNNs

arXiv: https://arxiv.org/abs/1703.10089 · HTML: https://mlsanity.com/paper/1703.10089/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Mar 2017.

## Built on by (most cited first, 7 of 7)

- [Dynamic Neural Networks: A Survey](https://mlsanity.com/paper/2102.04906/) (2021)
- [Regional Attention Network (RAN) for Head Pose and Fine-grained Gesture Recognition](https://mlsanity.com/paper/2101.06634/) (2021)
- [Leveraging Multiple Relations for Fashion Trend Forecasting Based on Social Media](https://mlsanity.com/paper/2105.03299/) (2021)
- [Explainable Parallel RCNN with Novel Feature Representation for Time Series Forecasting](https://mlsanity.com/paper/2305.04876/) (2023)
- [Movement Analytics: Current Status, Application to Manufacturing, and Future Prospects from an AI Perspective](https://mlsanity.com/paper/2210.01344/) (2022)
- [Seasonal Encoder-Decoder Architecture for Forecasting](https://mlsanity.com/paper/2207.04113/) (2022)
- [Public Transit Arrival Prediction: a Seq2Seq RNN Approach](https://mlsanity.com/paper/2210.01655/) (2022)

## Abstract (arXiv)

We propose here an extended attention model for sequence-to-sequence recurrent neural networks (RNNs) designed to capture (pseudo-)periods in time series. This extended attention model can be deployed on top of any RNN and is shown to yield state-of-the-art performance for time series forecasting on several univariate and multivariate time series.