mlsanityData to 14 Oct 2026

ELATE: Evolutionary Language model for Automated Time-series Engineering

arXiv 2508.14667 ↗First posted 20 Aug 2025cs.LG cs.AIcited by 3

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Abstract (arXiv)

Time-series prediction involves forecasting future values using machine learning models. Feature engineering, whereby existing features are transformed to make new ones, is critical for enhancing model performance, but is often manual and time-intensive. Existing automation attempts rely on exhaustive enumeration, which can be computationally costly and lacks domain-specific insights. We introduce ELATE (Evolutionary Language model for Automated Time-series Engineering), which leverages a language model within an evolutionary framework to automate feature engineering for time-series data. ELATE employs time-series statistical measures and feature importance metrics to guide and prune features, while the language model proposes new, contextually relevant feature transformations. Our experiments demonstrate that ELATE improves forecasting accuracy by an average of 8.4% across various domains.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 25 of its 63 references that are in the map. First posted on arXiv 20 Aug 2025.

Builds on

25 of 63 references.

References · 25

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2017A Unified Approach to Interpreting Model Predictionscited by 6,555
  3. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,166
  4. 2023Sparks of Artificial General Intelligence: Early experiments with GPT-4cited by 2,592
  5. 2020Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecastingcited by 2,169
  6. 2021Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecastingcited by 1,676
  7. 2023Time-LLM: Time Series Forecasting by Reprogramming Large Language Modelscited by 700
  8. 2018A Benchmark for Interpretability Methods in Deep Neural Networkscited by 476
  9. 2020Time Series Forecasting With Deep Learning: A Surveycited by 459
  10. 2018Consistent Individualized Feature Attribution for Tree Ensemblescited by 285
  11. 2024Evolution of Heuristics: Towards Efficient Automatic Algorithm Design Using Large Language Modelcited by 268
  12. 2024Are Language Models Actually Useful for Time Series Forecasting?cited by 168
13 more
  1. 2023Large Language Models for Automated Data Science: Introducing CAAFE for Context-Aware Automated Feature Engineeringcited by 127
  2. 2020True to the Model or True to the Data?cited by 126
  3. 2024Large Language Models for Time Series: A Surveycited by 114
  4. 2017Feature Engineering for Predictive Modeling using Reinforcement Learningcited by 78
  5. 2019The autofeat Python Library for Automated Feature Engineering and Selectioncited by 73
  6. 2021Granger Causality: A Review and Recent Advancescited by 63
  7. 2017One button machine for automating feature engineering in relational databasescited by 32
  8. 2016Distributed and parallel time series feature extraction for industrial big data applicationscited by 30
  9. 2024Evolutionary Large Language Model for Automated Feature Transformationcited by 29
  10. 2017Feature-based time-series analysiscited by 28
  11. 2019Techniques for Automated Machine Learningcited by 17
  12. 2020DIFER: Differentiable Automated Feature Engineeringcited by 16
  13. 2020VEST: Automatic Feature Engineering for Forecastingcited by 14

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026To See Far, Look Close: Evolutionary Forecasting for Long-term Time Seriescited by 1
  2. 2026Evolutionary Feature Engineering for Structured Datacited by 0
  3. 2026When Do Corrective Features Help? An Agent for Corrective Feature Discovery on Black-Box Forecasterscited by 0