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In the era of big data, practical applications in various domains continuallygenerate large-scale time-series data. Among them, some data show significantor potential periodicity characteristics, such as meteorological and financialdata. It is critical to efficiently identify the potential periodic patternsfrom massive time-series data and provide accurate predictions. In this paper,a Periodicity-based Parallel Time Series Prediction (PPTSP) algorithm forlarge-scale time-series data is proposed and implemented in the Apache Sparkcloud computing environment. To effectively handle the massive historicaldatasets, a Time Series Data Compression and Abstraction (TSDCA) algorithm ispresented, which can reduce the data scale as well as accurately extracting thecharacteristics. Based on this, we propose a Multi-layer Time Series PeriodicPattern Recognition (MTSPPR) algorithm using the Fourier Spectrum Analysis(FSA) method. In addition, a Periodicity-based Time Series Prediction (PTSP)algorithm is proposed. Data in the subsequent period are predicted based on allprevious period models, in which a time attenuation factor is introduced tocontrol the impact of different periods on the prediction results. Moreover, toimprove the performance of the proposed algorithms, we propose a parallelsolution on the Apache Spark platform, using the Streaming real-time computingmodule. To efficiently process the large-scale time-series datasets indistributed computing environments, Distributed Streams (DStreams) andResilient Distributed Datasets (RDDs) are used to store and calculate thesedatasets. Extensive experimental results show that our PPTSP algorithm hassignificant advantages compared with other algorithms in terms of predictionaccuracy and performance.

A Periodicity-based Parallel Time Series Prediction Algorithm in Cloud Computing Environments