关键词: 时间序列, 分段线性表示, 分段聚合近似, 双曲正切函数, 移动增强因子

Abstract: The traditional time series segmentation algorithm often ignore time characteristics of the time series, resulting in that fragmented results are not accurate enough. Therefore, a time series modeling representation algorithm based on hyperbolic tangent function constraint is proposed. The algorithm introduces the hyperbolic tangent function on the basis of the piecewise aggregation approximation and proposes the concept of the motion enhancement factor. Considering the impact of time, it completes the final time series segmentations by extracting the differences between the amounts of information in each subsequence. Experiments show that the algorithm has a small fitting error and can complete the macro similarity search by better utilization of the post-segmentation sequences and so on. Meanwhile, there is a characteristic that time series can grow dynamically. In the algorithm, both of the universality and accuracy are improved.

Key words: time series, piecewise linear representation, piecewise aggregation approximation, hyperbolic tangent function, mobile enhancement factor