# An Extreme-Adaptive Time Series Prediction Model Based on Probability-Enhanced LSTM Neural Networks

arXiv: https://arxiv.org/abs/2211.15891 · HTML: https://mlsanity.com/paper/2211.15891/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 43 references that are in the map.
- First posted on arXiv 29 Nov 2022.

## Builds on (most cited first, 5 of 5)

- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Show, Attend and Tell: Neural Image Caption Generation with Visual Attention](https://mlsanity.com/paper/1502.03044/) (2015)
- [DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks](https://mlsanity.com/paper/1704.04110/) (2017)
- [N-BEATS: Neural basis expansion analysis for interpretable time series forecasting](https://mlsanity.com/paper/1905.10437/) (2019)
- [Attention-Based Models for Speech Recognition](https://mlsanity.com/paper/1506.07503/) (2015)

## Built on by (most cited first, 9 of 9)

- [Hierarchical Classification Auxiliary Network for Time Series Forecasting](https://mlsanity.com/paper/2405.18975/) (2024)
- [PPGF: Probability Pattern-Guided Time Series Forecasting](https://mlsanity.com/paper/2502.12802/) (2025)
- [Times2D: Multi-Period Decomposition and Derivative Mapping for General Time Series Forecasting](https://mlsanity.com/paper/2504.00118/) (2025)
- [U-Cast: Learning Hierarchical Structures for High-Dimensional Time Series Forecasting](https://mlsanity.com/paper/2507.15119/) (2025)
- [Learning from Polar Representation: An Extreme-Adaptive Model for Long-Term Time Series Forecasting](https://mlsanity.com/paper/2312.08763/) (2023)
- [ReFine: Boosting Time Series Prediction of Extreme Events by Reweighting and Fine-tuning](https://mlsanity.com/paper/2409.14232/) (2024)
- [PFformer: A Position-Free Transformer Variant for Extreme-Adaptive Multivariate Time Series Forecasting](https://mlsanity.com/paper/2502.20571/) (2025)
- [xTime: Extreme Event Prediction with Hierarchical Knowledge Distillation and Expert Fusion](https://mlsanity.com/paper/2510.20651/) (2025)
- [Extreme Adaptive Transformer for Time Series Forecasting](https://mlsanity.com/paper/2607.02437/) (2026)

## Abstract (arXiv)

Forecasting time series with extreme events has been a challenging and prevalent research topic, especially when the time series data are affected by complicated uncertain factors, such as is the case in hydrologic prediction. Diverse traditional and deep learning models have been applied to discover the nonlinear relationships and recognize the complex patterns in these types of data. However, existing methods usually ignore the negative influence of imbalanced data, or severe events, on model training. Moreover, methods are usually evaluated on a small number of generally well-behaved time series, which does not show their ability to generalize. To tackle these issues, we propose a novel probability-enhanced neural network model, called NEC+, which concurrently learns extreme and normal prediction functions and a way to choose among them via selective back propagation. We evaluate the proposed model on the difficult 3-day ahead hourly water level prediction task applied to 9 reservoirs in California. Experimental results demonstrate that the proposed model significantly outperforms state-of-the-art baselines and exhibits superior generalization ability on data with diverse distributions.