A novel model for Time-Series Data Clustering Based on piecewise SVD and BIRCH for Stock Data Analysis on Hadoop Platform
Ibgtc Bowala, Mgnas Fernando · Advances in Science Technology and Engineering Systems Journal · 2017
With the rapid growth of financial markets, analyzers are paying more attention on predictions.Stock data are time series data, with huge amounts.Feasible solution for handling the increasing amount of data is to use a cluster for parallel processing, and Hadoop parallel computing platform is a typical representative.There are various statistical models for forecasting time series data, but accurate clusters are a prerequirement.Clustering analysis for time series data is one of the main methods for mining time series data for many other analysis processes.However, general clustering algorithms cannot perform clustering for time series data because series data has a special structure and a high dimensionality has highly co-related values due to high noise level.A novel model for time series clustering is presented using BIRCH, based on piecewise SVD, leading to a novel dimension reduction approach.Highly co-related features are handled using SVD with a novel approach for dimensionality reduction in order to keep co-related behavior optimal and then use BIRCH for clustering.The algorithm is a novel model that can handle massive time series data.Finally, this new model is successfully applied to real stock time series data of Yahoo finance with satisfactory results.