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Probabilistic Forecasting with Temporal Convolutional Neural Network

arXiv 1906.04397 ↗First posted 11 Jun 2019stat.ML cs.LGcited by 63

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Abstract (arXiv)

We present a probabilistic forecasting framework based on convolutional neural network for multiple related time series forecasting. The framework can be applied to estimate probability density under both parametric and non-parametric settings. More specifically, stacked residual blocks based on dilated causal convolutional nets are constructed to capture the temporal dependencies of the series. Combined with representation learning, our approach is able to learn complex patterns such as seasonality, holiday effects within and across series, and to leverage those patterns for more accurate forecasts, especially when historical data is sparse or unavailable. Extensive empirical studies are performed on several real-world datasets, including datasets from JD.com, China's largest online retailer. The results show that our framework outperforms other state-of-the-art methods in both accuracy and efficiency.

Key facts. Cited by 63 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Jun 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 11; 2022: 19; 2023: 16; 2024: 6; 2025: 9; 2026: 2.

63 papers.

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  1. 2024ORLM: A Customizable Framework in Training Large Models for Automated Optimization Modelingcited by 124
  2. 2023A Survey on Time-Series Pre-Trained Modelscited by 52
  3. 2021An Experimental Review on Deep Learning Architectures for Time Series Forecastingcited by 47
  4. 2021Global2Local: Efficient Structure Search for Video Action Segmentationcited by 42
  5. 2022TACTiS: Transformer-Attentional Copulas for Time Seriescited by 38
  6. 2021Learning Quantile Functions without Quantile Crossing for Distribution-free Time Series Forecastingcited by 32
  7. 2022A review of predictive uncertainty estimation with machine learningcited by 29
  8. 2022Ensemble Conformalized Quantile Regression for Probabilistic Time Series Forecastingcited by 27
  9. 2023TACTiS-2: Better, Faster, Simpler Attentional Copulas for Multivariate Time Seriescited by 25
  10. 2022Multivariate Time Series Forecasting with Latent Graph Inferencecited by 20
  11. 2022Epicasting: An Ensemble Wavelet Neural Network (EWNet) for Forecasting Epidemicscited by 16
  12. 2022Robust Probabilistic Time Series Forecastingcited by 15
38 more
  1. 2021Variance Reduced Training with Stratified Sampling for Forecasting Modelscited by 13
  2. 2022W-Transformers : A Wavelet-based Transformer Framework for Univariate Time Series Forecastingcited by 12
  3. 2021M5 Competition Uncertainty: Overdispersion, distributional forecasting, GAMLSS and beyondcited by 11
  4. 2022RF-Next: Efficient Receptive Field Search for Convolutional Neural Networkscited by 10
  5. 2024ElasTST: Towards Robust Varied-Horizon Forecasting with Elastic Time-Series Transformercited by 10
  6. 2025Deep Learning for Time Series Forecasting: A Surveycited by 10
  7. 2021Parameter Efficient Deep Probabilistic Forecastingcited by 8
  8. 2023Temporal Graph Neural Networks for Irregular Datacited by 7
  9. 2023Feature Programming for Multivariate Time Series Predictioncited by 7
  10. 2021Boosted Embeddings for Time Series Forecastingcited by 6
  11. 2022Probabilistic Decomposition Transformer for Time Series Forecastingcited by 6
  12. 2022Estimator: An Effective and Scalable Framework for Transportation Mode Classification over Trajectoriescited by 4
  13. 2025Epidemic-guided deep learning for spatiotemporal forecasting of Tuberculosis outbreakcited by 4
  14. 2021Temporal Convolutions for Multi-Step Quadrotor Motion Predictioncited by 3
  15. 2022Probabilistic forecasts of wind power generation in regions with complex topography using deep learning methods: An Arctic casecited by 3
  16. 2022An ensemble neural network approach to forecast Dengue outbreak based on climatic conditioncited by 3
  17. 2023Explainable Parallel RCNN with Novel Feature Representation for Time Series Forecastingcited by 3
  18. 2025TFMAdapter: Lightweight Instance-Level Adaptation of Foundation Models for Forecasting with Covariatescited by 2
  19. 2021Deep Probabilistic Time Series Forecasting using Augmented Recurrent Input for Dynamic Systemscited by 1
  20. 2022A Global Modeling Approach for Load Forecasting in Distribution Networkscited by 1
  21. 2022Sepsis Prediction with Temporal Convolutional Networkscited by 1
  22. 2022Time Series Forecasting Models Copy the Past: How to Mitigatecited by 1
  23. 2023GEANN: Scalable Graph Augmentations for Multi-Horizon Time Series Forecastingcited by 1
  24. 2023Probabilistic load forecasting with Reservoir Computingcited by 1
  25. 2023Utilizing VQ-VAE for End-to-End Health Indicator Generation in Predicting Rolling Bearing RULcited by 1
  26. 2023C2FAR: Coarse-to-Fine Autoregressive Networks for Precise Probabilistic Forecastingcited by 1
  27. 2024VAEneu: A New Avenue for VAE Application on Probabilistic Forecastingcited by 1
  28. 2025TCN-DPD: Parameter-Efficient Temporal Convolutional Networks for Wideband Digital Predistortioncited by 1
  29. 2026EpiCastBench: Datasets and Benchmarks for Multivariate Epidemic Forecastingcited by 1
  30. 2021QDCNN: Quantum Dilated Convolutional Neural Networkcited by 0
  31. 2021Achieving an Accurate Random Process Model for PV Power using Cheap Data: Leveraging the SDE and Public Weather Reportscited by 0
  32. 2022Probabilistic AutoRegressive Neural Networks for Accurate Long-range Forecastingcited by 0
  33. 2022Knowledge-based Deep Learning for Modeling Chaotic Systemscited by 0
  34. 2022Improving Accuracy Without Losing Interpretability: A ML Approach for Time Series Forecastingcited by 0
  35. 2022Leveraging Wastewater Monitoring for COVID-19 Forecasting in the US: a Deep Learning studycited by 0
  36. 2023A Concurrent CNN-RNN Approach for Multi-Step Wind Power Forecastingcited by 0
  37. 2023Two Steps Forward and One Behind: Rethinking Time Series Forecasting with Deep Learningcited by 0
  38. 2023A Deep Learning Approach Towards Generating High-fidelity Diverse Synthetic Battery Datasetscited by 0

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