arXiv:2505.14411v4 Announce Type: replace Abstract: Existing time series tokenization methods predominantly encode a constant number of samples into individual tokens. This inflexible approach can generate excessive tokens for even simple patterns like extended constant values, resulting in substantial computational overhead. Inspired by the success of byte pair encoding, we propose the first pattern-centric tokenization scheme for time series analysis. Based on a discrete vocabulary of frequent motifs, our method merges samples with underlying patterns into tokens, compressing time series ada
The increasing computational demands of time series analysis, particularly in AI applications, are driving the need for more efficient data processing methods.
This development could significantly reduce the computational overhead for training and deploying time series models, making AI applications more accessible and scalable.
Time series data processing can now move from uniform, sample-based tokenization to a more efficient, pattern-centric approach, reducing redundancy and improving model performance.
- · AI/ML researchers
- · Cloud computing providers
- · Industries relying on time series forecasting (e.g., finance, autonomous systems
- · Inefficient time series tokenization methods
- · Hardware providers whose value proposition is raw compute for brute-force time s
Reduced computational costs and accelerated development cycles for time series AI models.
Broader adoption of sophisticated time series forecasting in resource-constrained environments or for real-time applications.
The development of new AI agent architectures that leverage highly efficient temporal pattern recognition for decision-making.
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Read at arXiv cs.LG