  In the era of big data, practical applications in various domains continually
generate large-scale time-series data. Among them, some data show significant
or potential periodicity characteristics, such as meteorological and financial
data. It is critical to efficiently identify the potential periodic patterns
from massive time-series data and provide accurate predictions. In this paper,
a Periodicity-based Parallel Time Series Prediction (PPTSP) algorithm for
large-scale time-series data is proposed and implemented in the Apache Spark
cloud computing environment. To effectively handle the massive historical
datasets, a Time Series Data Compression and Abstraction (TSDCA) algorithm is
presented, which can reduce the data scale as well as accurately extracting the
characteristics. Based on this, we propose a Multi-layer Time Series Periodic
Pattern 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 all
previous period models, in which a time attenuation factor is introduced to
control the impact of different periods on the prediction results. Moreover, to
improve the performance of the proposed algorithms, we propose a parallel
solution on the Apache Spark platform, using the Streaming real-time computing
module. To efficiently process the large-scale time-series datasets in
distributed computing environments, Distributed Streams (DStreams) and
Resilient Distributed Datasets (RDDs) are used to store and calculate these
datasets. Extensive experimental results show that our PPTSP algorithm has
significant advantages compared with other algorithms in terms of prediction
accuracy and performance.

Machine Learning;Machine Learning