# Progressive Neural Network for Multi-Horizon Time Series Forecasting

arXiv: https://arxiv.org/abs/2310.19322 · HTML: https://mlsanity.com/paper/2310.19322/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 52 references that are in the map.
- First posted on arXiv 30 Oct 2023.

## Builds on (most cited first, 25 of 25)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting](https://mlsanity.com/paper/2012.07436/) (2020)
- [DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks](https://mlsanity.com/paper/1704.04110/) (2017)
- [Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks](https://mlsanity.com/paper/1506.03099/) (2015)
- [Sequence-Level Knowledge Distillation](https://mlsanity.com/paper/1606.07947/) (2016)
- [N-BEATS: Neural basis expansion analysis for interpretable time series forecasting](https://mlsanity.com/paper/1905.10437/) (2019)
- [Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting](https://mlsanity.com/paper/1907.00235/) (2019)
- [Non-Autoregressive Neural Machine Translation](https://mlsanity.com/paper/1711.02281/) (2017)
- [Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting](https://mlsanity.com/paper/2101.12072/) (2021)
- [A Multi-Horizon Quantile Recurrent Forecaster](https://mlsanity.com/paper/1711.11053/) (2017)
- [SSD-LM: Semi-autoregressive Simplex-based Diffusion Language Model for Text Generation and Modular Control](https://mlsanity.com/paper/2210.17432/) (2022)
- [Deep Encoder, Shallow Decoder: Reevaluating Non-autoregressive Machine Translation](https://mlsanity.com/paper/2006.10369/) (2020)
- [Fast Decoding in Sequence Models using Discrete Latent Variables](https://mlsanity.com/paper/1803.03382/) (2018)
- [Fully Non-autoregressive Neural Machine Translation: Tricks of the Trade](https://mlsanity.com/paper/2012.15833/) (2020)
- [Deep Factors for Forecasting](https://mlsanity.com/paper/1905.12417/) (2019)
- [Semi-Autoregressive Neural Machine Translation](https://mlsanity.com/paper/1808.08583/) (2018)
- [A Study of Non-autoregressive Model for Sequence Generation](https://mlsanity.com/paper/2004.10454/) (2020)
- [Learning to Recover from Multi-Modality Errors for Non-Autoregressive Neural Machine Translation](https://mlsanity.com/paper/2006.05165/) (2020)
- [SSDNet: State Space Decomposition Neural Network for Time Series Forecasting](https://mlsanity.com/paper/2112.10251/) (2021)
- [Semi-Autoregressive Transformer for Image Captioning](https://mlsanity.com/paper/2106.09436/) (2021)
- [Random vector functional link neural network based ensemble deep learning for short-term load forecasting](https://mlsanity.com/paper/2107.14385/) (2021)
- [Probabilistic Decomposition Transformer for Time Series Forecasting](https://mlsanity.com/paper/2210.17393/) (2022)
- [A Decomposition-Based Hybrid Ensemble CNN Framework for Driver Fatigue Recognition](https://mlsanity.com/paper/2203.09477/) (2022)
- [A Study on the Autoregressive and non-Autoregressive Multi-label Learning](https://mlsanity.com/paper/2012.01711/) (2020)

## Built on by (most cited first, 1 of 1)

- [Adaptive Convolutional Forecasting Network Based on Time Series Feature-Driven](https://mlsanity.com/paper/2405.12038/) (2024)

## Abstract (arXiv)

In this paper, we introduce ProNet, an novel deep learning approach designed for multi-horizon time series forecasting, adaptively blending autoregressive (AR) and non-autoregressive (NAR) strategies. Our method involves dividing the forecasting horizon into segments, predicting the most crucial steps in each segment non-autoregressively, and the remaining steps autoregressively. The segmentation process relies on latent variables, which effectively capture the significance of individual time steps through variational inference. In comparison to AR models, ProNet showcases remarkable advantages, requiring fewer AR iterations, resulting in faster prediction speed, and mitigating error accumulation. On the other hand, when compared to NAR models, ProNet takes into account the interdependency of predictions in the output space, leading to improved forecasting accuracy. Our comprehensive evaluation, encompassing four large datasets, and an ablation study, demonstrate the effectiveness of ProNet, highlighting its superior performance in terms of accuracy and prediction speed, outperforming state-of-the-art AR and NAR forecasting models.