Beyond Tokenization: Direct Timestep Embedding and Contrastive Alignment for Time-Series Question Answering
arXiv:2606.18986v1 Announce Type: cross Abstract: Recent advances in large language models (LLMs) have given rise to time-series question answering (TSQA), which formulates time-series analysis as natural-language question answering. However, directly feeding raw numerical series into LLMs suffers from a tokenization bottleneck: Byte Pair Encoding fragments continuous values into unstable tokens whose embeddings lack meaningful metric structure, resulting in the loss of magnitude, scale, and trend information. Prior methods use patch-based encoders that split the series into fixed windows, loc
The rapid development of large language models (LLMs) is creating demand for more effective integration with diverse data types, particularly time-series data which presents unique challenges to current tokenization methods.
Improving how LLMs process structured numerical data like time series is critical for expanding their utility from language tasks to more analytical and predictive applications across various industries.
This advancement proposes a new method for integrating time-series data into LLMs that preserves critical information lost during traditional tokenization, potentially enhancing model accuracy and robustness for time-series analysis.
- · AI/ML researchers
- · Data scientists
- · Predictive analytics companies
- · LLM developers
- · Traditional time-series analysis methods not integrated with LLMs
- · LLMs relying solely on BPE for numerical data
LLMs can more effectively perform question answering and analysis on complex time-series datasets.
Improved time-series question answering could accelerate insights and automation in finance, healthcare, and engineering.
The enhanced capability of LLMs to interpret and act on time-series data could contribute to the development of more sophisticated AI agents for data-driven decision-making.
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Read at arXiv cs.AI