DeepAI AI Chat
Log In Sign Up

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

10/17/2018
by   Jianguo Chen, et al.
New Paltz
Hunan University
University of Illinois at Chicago
ciit.net.pk
0

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.

READ FULL TEXT
05/30/2018

Efficient Sequential and Parallel Algorithms for Estimating Higher Order Spectra

Polyspectral estimation is a problem of great importance in the analysis...
10/17/2018

A Parallel Random Forest Algorithm for Big Data in a Spark Cloud Computing Environment

With the emergence of the big data age, the issue of how to obtain valua...
09/23/2018

Harvesting Time-Series Data from Service-Based Systems Hosted in MANETs

We are concerned with reliably harvesting data collected from service-ba...
08/09/2019

LSTM-based Flow Prediction

In this paper, a method of prediction on continuous time series variable...
10/02/2017

KV-match: An Efficient Subsequence Matching Approach for Large Scale Time Series

Time series data have exploded due to the popularity of new applications...
03/10/2016

Real time error detection in metal arc welding process using Artificial Neural Netwroks

Quality assurance in production line demands reliable weld joints. Human...