arXiv Machine Learning
QABBA: Symbolic Time-Series Compression via Integer-Quantized Aggregation
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QABBA: Error-Guaranteed Symbolic Time-Series Compression via Integer-Quantized Aggregation
QABBA is a quantized variant of the Adaptive Brownian Bridge-based Aggregation (ABBA) method that compresses long numerical time‑series into short symbolic sequences using integer‑quantized symbolic centers. It reduces parameter storage and enables integer arithmetic while providing rigorous error bounds—including a dimension‑free segment error, a time‑domain reconstruction error, a stability condition for symbolic assignment, and a bit‑allocation rule. The resulting symbolic strings can be fed directly into pretrained large language models, and experiments on several benchmark archives show a practical trade‑off among storage, reconstruction accuracy, and downstream predictive performance.
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