ELATE: Evolutionary Language model for Automated Time-series Engineering

• URL: http://arxiv.org/abs/2508.14667v1
• Date: Wed, 20 Aug 2025 12:36:29 GMT
• Title: ELATE: Evolutionary Language model for Automated Time-series Engineering
• Abstract summary: We introduce ELATE (Evolutionary Language model for Automated Time-series Engineering) to automate feature engineering for time-series data.<n>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.<n>Our experiments demonstrate that ELATE improves forecasting accuracy by an average of 8.4% across various domains.
• Score: 2.121963121603413
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• LeapTS: Rethinking Time Series Forecasting as Adaptive Multi-Horizon Scheduling [74.94985663101906]
  We present LeapTS, a novel framework that reformulates time series forecasting as a dynamic scheduling process over the prediction horizon.<n>We show that LeapTS improves overall forecasting performance by at least 7.4% while achieving a 2.6$times$ to 5.3$times$ inference speedup over representative Transformer-based models.
  arXiv  Detail & Related papers  (2026-05-11T09:54:02Z)
• Scaling Open-Ended Reasoning to Predict the Future [56.672065928345525]
  We train language models to make predictions on open-ended forecasting questions.<n>To scale up training data, we synthesize novel forecasting questions from global events reported in daily news.<n>We find calibration improvements from forecasting training generalize across popular benchmarks.
  arXiv  Detail & Related papers  (2025-12-31T18:59:51Z)
• AIDE: An Automatic Data Engine for Object Detection in Autonomous Driving [68.73885845181242]
  We propose an Automatic Data Engine (AIDE) that automatically identifies issues, efficiently curates data, improves the model through auto-labeling, and verifies the model through generation of diverse scenarios. We further establish a benchmark for open-world detection on AV datasets to comprehensively evaluate various learning paradigms, demonstrating our method's superior performance at a reduced cost.
  arXiv  Detail & Related papers  (2024-03-26T04:27:56Z)
• Chronos: Learning the Language of Time Series [79.38691251254173]
  Chronos is a framework for pretrained probabilistic time series models. We show that Chronos models can leverage time series data from diverse domains to improve zero-shot accuracy on unseen forecasting tasks.
  arXiv  Detail & Related papers  (2024-03-12T16:53:54Z)
• Timer: Generative Pre-trained Transformers Are Large Time Series Models [83.03091523806668]
  This paper aims at the early development of large time series models (LTSM) During pre-training, we curate large-scale datasets with up to 1 billion time points. To meet diverse application needs, we convert forecasting, imputation, and anomaly detection of time series into a unified generative task.
  arXiv  Detail & Related papers  (2024-02-04T06:55:55Z)
• auto-sktime: Automated Time Series Forecasting [18.640815949661903]
  We introduce auto-sktime, a novel framework for automated time series forecasting. The proposed framework uses the power of automated machine learning (AutoML) techniques to automate the creation of the entire forecasting pipeline. Experimental results on 64 diverse real-world time series datasets demonstrate the effectiveness and efficiency of the framework.
  arXiv  Detail & Related papers  (2023-12-13T21:34:30Z)
• Time-LLM: Time Series Forecasting by Reprogramming Large Language Models [110.20279343734548]
  Time series forecasting holds significant importance in many real-world dynamic systems. We present Time-LLM, a reprogramming framework to repurpose large language models for time series forecasting. Time-LLM is a powerful time series learner that outperforms state-of-the-art, specialized forecasting models.
  arXiv  Detail & Related papers  (2023-10-03T01:31:25Z)
• EAMDrift: An interpretable self retrain model for time series [0.0]
  We present EAMDrift, a novel method that combines forecasts from multiple individual predictors by weighting each prediction according to a performance metric. EAMDrift is designed to automatically adapt to out-of-distribution patterns in data and identify the most appropriate models to use at each moment. Our study on real-world datasets shows that EAMDrift outperforms individual baseline models by 20% and achieves comparable accuracy results to non-interpretable ensemble models.
  arXiv  Detail & Related papers  (2023-05-31T13:25:26Z)
• Entity Aware Modelling: A Survey [22.32009539611539]
  Recent machine learning advances have led to new state-of-the-art response prediction models. Models built at a population level often lead to sub-optimal performance in many personalized prediction settings. In personalized prediction, the goal is to incorporate inherent characteristics of different entities to improve prediction performance.
  arXiv  Detail & Related papers  (2023-02-16T16:33:33Z)
• Machine Learning Algorithms for Time Series Analysis and Forecasting [0.0]
  Time series data is being used everywhere, from sales records to patients' health evolution metrics. Various statistical and deep learning models have been considered, notably, ARIMA, Prophet and LSTMs. Our work can be used by anyone to develop a good understanding of the forecasting process, and to identify various state of the art models which are being used today.
  arXiv  Detail & Related papers  (2022-11-25T22:12:03Z)
• Temporal Attention for Language Models [24.34396762188068]
  We extend the key component of the transformer architecture, i.e., the self-attention mechanism, and propose temporal attention. temporal attention can be applied to any transformer model and requires the input texts to be accompanied with their relevant time points. We leverage these representations for the task of semantic change detection. Our proposed model achieves state-of-the-art results on all the datasets.
  arXiv  Detail & Related papers  (2022-02-04T11:55:34Z)
• Merlion: A Machine Learning Library for Time Series [73.46386700728577]
  Merlion is an open-source machine learning library for time series. It features a unified interface for models and datasets for anomaly detection and forecasting. Merlion also provides a unique evaluation framework that simulates the live deployment and re-training of a model in production.
  arXiv  Detail & Related papers  (2021-09-20T02:03:43Z)
• Automated Machine Learning Techniques for Data Streams [91.3755431537592]
  This paper surveys the state-of-the-art open-source AutoML tools, applies them to data collected from streams, and measures how their performance changes over time. The results show that off-the-shelf AutoML tools can provide satisfactory results but in the presence of concept drift, detection or adaptation techniques have to be applied to maintain the predictive accuracy over time.
  arXiv  Detail & Related papers  (2021-06-14T11:42:46Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.