Abstract: Against the backdrop of explosive growth in floating-point time series data within domains such as industrial IoT and financial technology,significant challenges have emerged for data storage and transmission,rendering floating-point time series data compression of paramount importance.Typically,time-series data suffers from encoding redundancy caused by outlier contamination,disruption of temporal continuity due to missing values,and failure of prediction models triggered by unstructured time series.Moreover,single prediction models exhibit marked performance disparities when dealing with linear short-cycle scenarios versus sparse and complex ones.To address these issues,this paper proposes the ALHC algorithm.Aiming to meet the requirements of floating-point compression concerning data accuracy,continuity,distribution characteristics,and residual calculation,a targeted data cleaning process is designed.A hybrid architecture is constructed,dynamically switching between a TCN-LSTM neural network predictor and an adaptive linear predictor based on the LMS algorithm,enabling adaptation to different scenarios.The adaptive predictor is employed as a post-processing module for residual correction,enhancing prediction accuracy,reducing residuals,and improving entropy coding efficiency.Experimental evaluations of the proposed compression algorithm on 14 public time series datasets demonstrate that under lossless conditions,the average compression ratio reaches 0.24.

Key words: Floating-point time series, Lossless compression, Preprocessing link, TCN-LSTM, Hybrid prediction architecture, Adaptive linear predictor