Multidimensional Time Series Big Data Mining Method Based on Artificial Bee Colony Optimization
XU Shengchao
MAO Mingyang
CHEN Gang
Abstract：Because multidimensional time series data has high dimensions,it is difficult to achieve ideal mining results.Therefore,a multidimensional time series big data mining method based on artificial bee colony optimization algorithm is proposed.The multi-dimensional time series data is transformed into the form of pattern representation by extreme value segmentation meth-od,and then the multi-dimensional time series data is transformed into the spectrum space by Haar wavelet transform.The frequen-cy and position information of the data are changed to obtain the characteristic coefficient of the data.The feature extraction algo-rithm is used to find out the useful feature coefficients and filter out the useless ones directly.The objective function is constructed for the extracted characteristic coefficients,the local optimization of the objective function is completed based on the artificial bee colony optimization algorithm,the optimal solution is found on the basis of the fitness function,and the multi-dimensional time se-ries data mining is completed.The experimental results show that the data mining effect of the proposed method is good,which can effectively shorten the data mining time and improve the data mining accuracy.
Keywords：artificial bee colony optimizationmultidimensional timingdata miningcharacteristic coefficientspectrum spacefitness function
Publication Date：2025-11-20
Online Publishing Date：2026-01-28(First online date of this platform, not the publication date of the document)
Pages：7( 3098-3103,3126 )