Time series decomposition and predictive analytics using MapReduce framework

Abstract

The recent development in precision agriculture, a large amount of data are generated by site-specific weather stations which will demand a platform for the processing and predictive weather analytics. The sophisticated methodology to solve large amount of data handling problem and process data in a small time is important. In this study, future conditions are predicted from weather stations large data by proposing the predictive approaches based on time series and neural network using MapReduce programming model. We have proposed predictive analytics approaches including the modules, i.e., analysis and decomposition, classification, and prediction. The time series based decomposition approach is proposed to decompose and find out the trend, regular and sophisticated components. The linear components are handled by time series MapReduce based Autoregressive Integrated Moving Average (M-ARIMA) model and nonlinear components are handled by M-K-Nearest Neighbors (M-KNN) model. In addition, the MapReduce-based Hybrid Model (M-HM) was proposed which will use the advantages of time series and neural network to increase prediction accuracy. The study verifies the effectiveness of proposed model over the regular and randomness component of the data. The performance measures and statistical test are performed to validate and check data consistency. In addition, excellent speed-up, scale-up, and size-up were tested by changing the size of data set. However, when the data size increases, the average execution time is reduced by using the MapReduce-based approach over the multiple-node workers.