Main Reference PaperMulti-step forecasting for big data time series based on ensemble learning, Knowledge-Based Systems, 2018 [Python/Apache Spark]
  • An ensemble of regression method is proposed for predicting big data time series. A proposed ensemble method computes the weights for each ensemble member using a least square method. The ensemble member algorithms are decision tree, gradient boosted trees and random forest. The highest weight of ensemble members provides more accuracy.

Description
  • An ensemble of regression method is proposed for predicting big data time series. A proposed ensemble method computes the weights for each ensemble member using a least square method. The ensemble member algorithms are decision tree, gradient boosted trees and random forest. The highest weight of ensemble members provides more accuracy.

  • Reducing the execution time of jobs.

  • To predict the big time series.

Aim & Objectives
  • Reducing the execution time of jobs.

  • To predict the big time series.

  • The proposed scheme includes other types of prediction models to the ensemble, which are suitable for big data, in order to increase the diversity among the ensemble members.

Contribution
  • The proposed scheme includes other types of prediction models to the ensemble, which are suitable for big data, in order to increase the diversity among the ensemble members.

  • M.E / M.Tech / MS / Ph.D.- Customized according to the client requirements.

Project Recommended For
  • M.E / M.Tech / MS / Ph.D.- Customized according to the client requirements.

  • No Readymade Projects-project delivery Depending on the complexity of the project and requirements.

Order To Delivery
  • No Readymade Projects-project delivery Depending on the complexity of the project and requirements.

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