# Autoregressive Convolutional Neural Networks for Asynchronous Time Series

arXiv: https://arxiv.org/abs/1703.04122 · HTML: https://mlsanity.com/paper/1703.04122/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Mar 2017.

## Built on by (most cited first, 30 of 31)

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- [EgPDE-Net: Building Continuous Neural Networks for Time Series Prediction with Exogenous Variables](https://mlsanity.com/paper/2208.01913/) (2022)
- [Deep Learning for Options Trading: An End-To-End Approach](https://mlsanity.com/paper/2407.21791/) (2024)
- [Heteroscedastic Temporal Variational Autoencoder For Irregular Time Series](https://mlsanity.com/paper/2107.11350/) (2021)
- [CaSS: A Channel-aware Self-supervised Representation Learning Framework for Multivariate Time Series Classification](https://mlsanity.com/paper/2203.04298/) (2022)
- [Sepsis Prediction with Temporal Convolutional Networks](https://mlsanity.com/paper/2205.15492/) (2022)
- [Exploring Sectoral Profitability in the Indian Stock Market Using Deep Learning](https://mlsanity.com/paper/2407.01572/) (2024)
- [STAN: Smooth Transition Autoregressive Networks](https://mlsanity.com/paper/2501.18699/) (2025)
- [Stock Market Telepathy: Graph Neural Networks Predicting the Secret Conversations between MINT and G7 Countries](https://mlsanity.com/paper/2506.01945/) (2025)

## Abstract (arXiv)

We propose Significance-Offset Convolutional Neural Network, a deep convolutional network architecture for regression of multivariate asynchronous time series. The model is inspired by standard autoregressive (AR) models and gating mechanisms used in recurrent neural networks. It involves an AR-like weighting system, where the final predictor is obtained as a weighted sum of adjusted regressors, while the weights are datadependent functions learnt through a convolutional network. The architecture was designed for applications on asynchronous time series and is evaluated on such datasets: a hedge fund proprietary dataset of over 2 million quotes for a credit derivative index, an artificially generated noisy autoregressive series and UCI household electricity consumption dataset. The proposed architecture achieves promising results as compared to convolutional and recurrent neural networks.