# Probabilistic Forecasting with Temporal Convolutional Neural Network

arXiv: https://arxiv.org/abs/1906.04397 · HTML: https://mlsanity.com/paper/1906.04397/

## Key facts

- Cited by 63 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jun 2019.

## Built on by (most cited first, 30 of 63)

- [ORLM: A Customizable Framework in Training Large Models for Automated Optimization Modeling](https://mlsanity.com/paper/2405.17743/) (2024)
- [A Survey on Time-Series Pre-Trained Models](https://mlsanity.com/paper/2305.10716/) (2023)
- [An Experimental Review on Deep Learning Architectures for Time Series Forecasting](https://mlsanity.com/paper/2103.12057/) (2021)
- [Global2Local: Efficient Structure Search for Video Action Segmentation](https://mlsanity.com/paper/2101.00910/) (2021)
- [TACTiS: Transformer-Attentional Copulas for Time Series](https://mlsanity.com/paper/2202.03528/) (2022)
- [Learning Quantile Functions without Quantile Crossing for Distribution-free Time Series Forecasting](https://mlsanity.com/paper/2111.06581/) (2021)
- [A review of predictive uncertainty estimation with machine learning](https://mlsanity.com/paper/2209.08307/) (2022)
- [Ensemble Conformalized Quantile Regression for Probabilistic Time Series Forecasting](https://mlsanity.com/paper/2202.08756/) (2022)
- [TACTiS-2: Better, Faster, Simpler Attentional Copulas for Multivariate Time Series](https://mlsanity.com/paper/2310.01327/) (2023)
- [Multivariate Time Series Forecasting with Latent Graph Inference](https://mlsanity.com/paper/2203.03423/) (2022)
- [Epicasting: An Ensemble Wavelet Neural Network (EWNet) for Forecasting Epidemics](https://mlsanity.com/paper/2206.10696/) (2022)
- [Robust Probabilistic Time Series Forecasting](https://mlsanity.com/paper/2202.11910/) (2022)
- [Variance Reduced Training with Stratified Sampling for Forecasting Models](https://mlsanity.com/paper/2103.02062/) (2021)
- [W-Transformers : A Wavelet-based Transformer Framework for Univariate Time Series Forecasting](https://mlsanity.com/paper/2209.03945/) (2022)
- [M5 Competition Uncertainty: Overdispersion, distributional forecasting, GAMLSS and beyond](https://mlsanity.com/paper/2107.06675/) (2021)
- [RF-Next: Efficient Receptive Field Search for Convolutional Neural Networks](https://mlsanity.com/paper/2206.06637/) (2022)
- [ElasTST: Towards Robust Varied-Horizon Forecasting with Elastic Time-Series Transformer](https://mlsanity.com/paper/2411.01842/) (2024)
- [Deep Learning for Time Series Forecasting: A Survey](https://mlsanity.com/paper/2503.10198/) (2025)
- [Parameter Efficient Deep Probabilistic Forecasting](https://mlsanity.com/paper/2112.02905/) (2021)
- [Temporal Graph Neural Networks for Irregular Data](https://mlsanity.com/paper/2302.08415/) (2023)
- [Feature Programming for Multivariate Time Series Prediction](https://mlsanity.com/paper/2306.06252/) (2023)
- [Boosted Embeddings for Time Series Forecasting](https://mlsanity.com/paper/2104.04781/) (2021)
- [Probabilistic Decomposition Transformer for Time Series Forecasting](https://mlsanity.com/paper/2210.17393/) (2022)
- [Estimator: An Effective and Scalable Framework for Transportation Mode Classification over Trajectories](https://mlsanity.com/paper/2212.05502/) (2022)
- [Epidemic-guided deep learning for spatiotemporal forecasting of Tuberculosis outbreak](https://mlsanity.com/paper/2502.10786/) (2025)
- [Temporal Convolutions for Multi-Step Quadrotor Motion Prediction](https://mlsanity.com/paper/2110.04182/) (2021)
- [Probabilistic forecasts of wind power generation in regions with complex topography using deep learning methods: An Arctic case](https://mlsanity.com/paper/2203.07080/) (2022)
- [An ensemble neural network approach to forecast Dengue outbreak based on climatic condition](https://mlsanity.com/paper/2212.08323/) (2022)
- [Explainable Parallel RCNN with Novel Feature Representation for Time Series Forecasting](https://mlsanity.com/paper/2305.04876/) (2023)
- [TFMAdapter: Lightweight Instance-Level Adaptation of Foundation Models for Forecasting with Covariates](https://mlsanity.com/paper/2509.13906/) (2025)

## 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.