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Autoregressive Convolutional Neural Networks for Asynchronous Time Series

arXiv 1703.04122 ↗First posted 12 Mar 2017cs.LGcited by 31

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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.

Key facts. Cited by 31 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Mar 2017.

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Citing papers per year: 2021: 10; 2022: 5; 2023: 6; 2024: 6; 2025: 4.

31 papers.

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  1. 2023Generative Time Series Forecasting with Diffusion, Denoise, and Disentanglementcited by 89
  2. 2024A Temporal Kolmogorov-Arnold Transformer for Time Series Forecastingcited by 50
  3. 2021Do We Really Need Deep Learning Models for Time Series Forecasting?cited by 45
  4. 2021Dynamic Gaussian Mixture based Deep Generative Model For Robust Forecasting on Sparse Multivariate Time Seriescited by 30
  5. 2021Variable-rate discrete representation learningcited by 24
  6. 2021A Multi-view Multi-task Learning Framework for Multi-variate Time Series Forecastingcited by 21
  7. 2021Accurate Stock Price Forecasting Using Robust and Optimized Deep Learning Modelscited by 15
  8. 2024SigKAN: Signature-Weighted Kolmogorov-Arnold Networks for Time Seriescited by 13
  9. 2023A Systematic Review for Transformer-based Long-term Series Forecastingcited by 10
  10. 2021Stock Portfolio Optimization Using a Deep Learning LSTM Modelcited by 9
  11. 2022A Causal Approach to Detecting Multivariate Time-series Anomalies and Root Causescited by 8
  12. 2023Bayesian neural networks via MCMC: a Python-based tutorialcited by 8
19 more
  1. 2023Spatio-Temporal Momentum: Jointly Learning Time-Series and Cross-Sectional Strategiescited by 6
  2. 2024Deep Coupling Network For Multivariate Time Series Forecastingcited by 6
  3. 2023Centralised rehearsal of decentralised cooperation: Multi-agent reinforcement learning for the scalable coordination of residential energy flexibilitycited by 5
  4. 2025Applying Informer for Option Pricing: A Transformer-Based Approachcited by 5
  5. 2024A Temporal Linear Network for Time Series Forecastingcited by 4
  6. 2021Synergetic Learning of Heterogeneous Temporal Sequences for Multi-Horizon Probabilistic Forecastingcited by 3
  7. 2021Analysis of Sectoral Profitability of the Indian Stock Market Using an LSTM Regression Modelcited by 3
  8. 2022Neural Additive Models for Nowcastingcited by 3
  9. 2023A predictive physics-aware hybrid reduced order model for reacting flowscited by 3
  10. 2021Optimum Risk Portfolio and Eigen Portfolio: A Comparative Analysis Using Selected Stocks from the Indian Stock Marketcited by 2
  11. 2022EgPDE-Net: Building Continuous Neural Networks for Time Series Prediction with Exogenous Variablescited by 2
  12. 2024Deep Learning for Options Trading: An End-To-End Approachcited by 2
  13. 2021Heteroscedastic Temporal Variational Autoencoder For Irregular Time Seriescited by 1
  14. 2022CaSS: A Channel-aware Self-supervised Representation Learning Framework for Multivariate Time Series Classificationcited by 1
  15. 2022Sepsis Prediction with Temporal Convolutional Networkscited by 1
  16. 2024Exploring Sectoral Profitability in the Indian Stock Market Using Deep Learningcited by 0
  17. 2025STAN: Smooth Transition Autoregressive Networkscited by 0
  18. 2025Stock Market Telepathy: Graph Neural Networks Predicting the Secret Conversations between MINT and G7 Countriescited by 0
  19. 2025S&P 500 Stock's Movement Prediction using CNNcited by 0