# EA-LSTM: Evolutionary Attention-based LSTM for Time Series Prediction

arXiv: https://arxiv.org/abs/1811.03760 · HTML: https://mlsanity.com/paper/1811.03760/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Nov 2018.

## Built on by (most cited first, 26 of 26)

- [Label-efficient Time Series Representation Learning: A Review](https://mlsanity.com/paper/2302.06433/) (2023)
- [Parallel Spatio-Temporal Attention-Based TCN for Multivariate Time Series Prediction](https://mlsanity.com/paper/2203.00971/) (2022)
- [EvoTorch: Scalable Evolutionary Computation in Python](https://mlsanity.com/paper/2302.12600/) (2023)
- [Learning-based NLOS Detection and Uncertainty Prediction of GNSS Observations with Transformer-Enhanced LSTM Network](https://mlsanity.com/paper/2309.00480/) (2023)
- [Modeling Continuous Time Sequences with Intermittent Observations using Marked Temporal Point Processes](https://mlsanity.com/paper/2206.12414/) (2022)
- [C-SHAP for time series: An approach to high-level temporal explanations](https://mlsanity.com/paper/2504.11159/) (2025)
- [DeepExpress: Heterogeneous and Coupled Sequence Modeling for Express Delivery Prediction](https://mlsanity.com/paper/2108.08170/) (2021)
- [LSTM-RPA: A Simple but Effective Long Sequence Prediction Algorithm for Music Popularity Prediction](https://mlsanity.com/paper/2110.15790/) (2021)
- [A new hazard event classification model via deep learning and multifractal](https://mlsanity.com/paper/2209.05263/) (2022)
- [Modeling Time-Series and Spatial Data for Recommendations and Other Applications](https://mlsanity.com/paper/2212.13259/) (2022)
- [Mitigating Cold-start Forecasting using Cold Causal Demand Forecasting Model](https://mlsanity.com/paper/2306.09261/) (2023)
- [On the choice of the non-trainable internal weights in random feature maps](https://mlsanity.com/paper/2408.03626/) (2024)
- [X-MethaneWet: A Cross-scale Global Wetland Methane Emission Benchmark Dataset for Advancing Science Discovery with AI](https://mlsanity.com/paper/2505.18355/) (2025)
- [Embedded feature selection in LSTM networks with multi-objective evolutionary ensemble learning for time series forecasting](https://mlsanity.com/paper/2312.17517/) (2023)
- [Multiple Population Alternate Evolution Neural Architecture Search](https://mlsanity.com/paper/2403.07035/) (2024)
- [Holistic Explainable AI (H-XAI): Extending Transparency Beyond Developers in AI-Driven Decision Making](https://mlsanity.com/paper/2508.05792/) (2025)
- [On Identifying Why and When Foundation Models Perform Well on Time-Series Forecasting Using Automated Explanations and Rating](https://mlsanity.com/paper/2508.20437/) (2025)
- [AgroFlux: A Spatial-Temporal Benchmark for Carbon and Nitrogen Flux Prediction in Agricultural Ecosystems](https://mlsanity.com/paper/2602.01614/) (2026)
- [Classification of multivariate weakly-labelled time-series with attention](https://mlsanity.com/paper/2102.08245/) (2021)
- [Transfer-Recursive-Ensemble Learning for Multi-Day COVID-19 Prediction in India using Recurrent Neural Networks](https://mlsanity.com/paper/2108.09131/) (2021)
- [pmSensing: A Participatory Sensing Network for Predictive Monitoring of Particulate Matter](https://mlsanity.com/paper/2111.11441/) (2021)
- [Time Series Prediction about Air Quality using LSTM-Based Models: A Systematic Mapping](https://mlsanity.com/paper/2111.11848/) (2021)
- [A Differential Attention Fusion Model Based on Transformer for Time Series Forecasting](https://mlsanity.com/paper/2202.11402/) (2022)
- [EAMDrift: An interpretable self retrain model for time series](https://mlsanity.com/paper/2305.19837/) (2023)
- [Encoding Seasonal Climate Predictions for Demand Forecasting with Modular Neural Network](https://mlsanity.com/paper/2309.02248/) (2023)
- [Toward accurate RUL and SoH estimation using reinforced graph-based physics-informed neural networks enhanced with dynamic weights](https://mlsanity.com/paper/2507.09766/) (2025)

## Abstract (arXiv)

Time series prediction with deep learning methods, especially long short-term memory neural networks (LSTMs), have scored significant achievements in recent years. Despite the fact that the LSTMs can help to capture long-term dependencies, its ability to pay different degree of attention on sub-window feature within multiple time-steps is insufficient. To address this issue, an evolutionary attention-based LSTM training with competitive random search is proposed for multivariate time series prediction. By transferring shared parameters, an evolutionary attention learning approach is introduced to the LSTMs model. Thus, like that for biological evolution, the pattern for importance-based attention sampling can be confirmed during temporal relationship mining. To refrain from being trapped into partial optimization like traditional gradient-based methods, an evolutionary computation inspired competitive random search method is proposed, which can well configure the parameters in the attention layer. Experimental results have illustrated that the proposed model can achieve competetive prediction performance compared with other baseline methods.