An optimization in big data time series prediction method by Parzen estimation with Spark

Abstract

With the development and change of big data related technologies, more and more large amounts of data need to be analyzed. Now there are companies like Google, Yahoo, etc. Frameworks such as MapReduce, Hadoop, Spark, etc. are developed for processing large amounts of data. In this paper, relevant discussions and researches are carried out on time series forecasting under the new era of big data. Now there are time series forecasting methods based on map reduce, Hadoop, spark data processing framework, including nearest neighbor distribution method, neural network method, etc., which have made quite good achievements in big data time series forecasting. By reading the relevant research literature, it is universally acknowledged that the Sparks framework has good application prospects and potential in predicting big data time series. As a result, this paper is mainly aimed at the optimization and improvement of the big data time series forecasting method on the basis of the spark framework. The author noticed that most of the default configurations of spark clusters are generated by default or automatically, rather than the optimal solution obtained after algorithm optimization, so there is still room for improvement in this regard. In this regard, this paper proposes a kernel method for visual data processing of related configurations and parameters, and then optimizes the default data configuration as much as possible to improve the accuracy and feasibility of the big data time series prediction method on the basis of the spark framework. In this paper, the optimized scheme is used to forecast the domestic electricity consumption in the past five years, and the results show that the optimized scheme has a good improvement performance on the basis of the original method.