关键词: 时间序列分解, Spark, 云计算, 并行计算, STL, SSA

Abstract:

This paper proposes a parallel time series decomposition model based on Spark to handle the challenges of time series decomposition in the era of big data. Specifically, the parallel algorithm consists of three steps: Firstly,  a time series is split into a sequence of sub-time series while adding some redundant data into the sub-time series to protect these sub-time series being polluted. Secondly, each segmented sub-time series (with redundant information) is transferred to one work node on Spark for analyzing by using some time series decomposition algorithms. Thirdly, the redundant parts in the analysis result of each sub-time series are removed and the results are integrated. Experiments are conducted to evaluate the model, the results demonstrate its effectiveness and accuracy on large-scale time series datasets.

Key words: time series decomposition, Spark, cloud computing, parallel computing, STL, SSA