量化符号时间序列逼近

时间序列在诸多科学和工程领域中随处可见，例如信号处理、生物信息学和天文学。先前的研究已验证了符号化时间序列表示在各种工程应用中的有效性，由于其存储效率和数量减少。最新的符号聚合逼近技术ABBA被证明能够保留时间序列的关键形状信息，并改善下游应用，例如关于时间序列预测和异常检测的神经网络推断。 motivated by the emergence of high-performance hardware which enables efficient computation for low bit-width representations, we present a new quantization-based ABBA symbolic approximation technique, QABBA, which exhibits improved storage efficiency while retaining the original speed and accuracy of symbolic reconstruction. We prove an upper bound for the error arising from quantization and discuss how the number of bits should be chosen to balance this with other errors. An application of QABBA with large language models (LLMs) for time series regression is also presented, and its utility is investigated. By representing the symbolic chain of patterns on time series, QABBA not only avoids the training of embedding from scratch, but also achieves a new state-of-the-art on Monash regression dataset. The symbolic approximation to the time series offers a more efficient way to fine-tune LLMs on the time series regression task which contains various application domains. We further present a set of extensive experiments performed across various well-established datasets to demonstrate the advantages of the QABBA method for symbolic approximation.