# Probabilistic Forecasting with Temporal Convolutional Neural Network

**Authors:** Yitian Chen, Yanfei Kang, Yixiong Chen, Zizhuo Wang  
**arXiv:** [1906.04397](https://arxiv.org/abs/1906.04397)  
**Date:** 2019-06-11  
**Summary page:** https://www.summarizepaper.com/en/paper/1906.04397v3/

## Key points

- The paper presents a novel probabilistic forecasting framework based on convolutional neural network (CNN) for multiple related time series forecasting.
- The proposed framework can estimate probability density under both parametric and non-parametric settings.
- Stacked residual blocks based on dilated causal convolutional nets are used to capture the temporal dependencies of the series.
- Representation learning is combined with the approach to learn complex patterns such as seasonality and holiday effects within and across series, which can be leveraged for more accurate forecasts, especially when historical data is sparse or unavailable.
- Extensive empirical studies were performed on several real-world datasets, including datasets from JD.com, China's largest online retailer.
- The results show that their framework outperforms other state-of-the-art methods in both accuracy and efficiency. Specifically, it achieves better performance than traditional statistical models such as ARIMA and state-of-the-art deep learning models such as LSTM-based approaches.
- The paper highlights the importance of probabilistic forecasting in various applications such as finance, energy management, and transportation planning.
- The proposed framework provides a flexible solution that can handle multiple related time series while incorporating uncertainty into the forecasted results.
- Overall, this work contributes to advancing the field of time series forecasting by introducing a novel CNN-based approach that improves upon existing methods in terms of accuracy and efficiency.

## Summary

The paper titled "Probabilistic Forecasting with Temporal Convolutional Neural Network" presents a novel probabilistic forecasting framework based on convolutional neural network (CNN) for multiple related time series forecasting. The proposed framework is capable of estimating probability density under both parametric and non-parametric settings. The authors construct stacked residual blocks based on dilated causal convolutional nets to capture the temporal dependencies of the series. Additionally, representation learning is combined with the approach to learn complex patterns such as seasonality and holiday effects within and across series, which can be leveraged for more accurate forecasts, especially when historical data is sparse or unavailable. The authors perform extensive empirical studies on several real-world datasets, including datasets from JD.com, China's largest online retailer. The results show that their framework outperforms other state-of-the-art methods in both accuracy and efficiency. Specifically, the proposed method achieves better performance than traditional statistical models such as ARIMA and state-of-the-art deep learning models such as LSTM-based approaches. The paper highlights the importance of probabilistic forecasting in various applications such as finance, energy management, and transportation planning. The proposed framework provides a flexible solution that can handle multiple related time series while incorporating uncertainty into the forecasted results. Overall, this work contributes to advancing the field of time series forecasting by introducing a novel CNN-based approach that improves upon existing methods in terms of accuracy and efficiency.