mlsanityData to 7 Oct 2026

VITRO: Vocabulary Inversion for Time-series Representation Optimization

arXiv 2412.17921 ↗First posted 23 Dec 2024cs.LG cs.CLcited by 1

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Abstract (arXiv)

Although LLMs have demonstrated remarkable capabilities in processing and generating textual data, their pre-trained vocabularies are ill-suited for capturing the nuanced temporal dynamics and patterns inherent in time series. The discrete, symbolic nature of natural language tokens, which these vocabularies are designed to represent, does not align well with the continuous, numerical nature of time series data. To address this fundamental limitation, we propose VITRO. Our method adapts textual inversion optimization from the vision-language domain in order to learn a new time series per-dataset vocabulary that bridges the gap between the discrete, semantic nature of natural language and the continuous, numerical nature of time series data. We show that learnable time series-specific pseudo-word embeddings represent time series data better than existing general language model vocabularies, with VITRO-enhanced methods achieving state-of-the-art performance in long-term forecasting across most datasets.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 15 of its 22 references that are in the map. First posted on arXiv 23 Dec 2024.

Builds on

15 of 22 references.

References · 15

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
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3 more
  1. 2023A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detectioncited by 145
  2. 2022Domain Adversarial Spatial-Temporal Network: A Transferable Framework for Short-term Traffic Forecasting across Citiescited by 38
  3. 2023SaDI: A Self-adaptive Decomposed Interpretable Framework for Electric Load Forecasting under Extreme Eventscited by 21

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Deep TPC: Temporal-Prior Conditioning for Time Series Forecastingcited by 0