Main Reference PaperTime series decomposition and predictive analytics using MapReduce framework, Expert Systems With Applications, 2018 [Java/Hadoop]
  • 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) models are successfully predicted the de-composed, regular and stochastic components. The predicted results are used to identify forecasting behavior of the weather stations.

Description
  • 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) models are successfully predicted the de-composed, regular and stochastic components. The predicted results are used to identify forecasting behavior of the weather stations.

  • To predict the forecasting behavior.

  • To improve the prediction accuracy.

Aim & Objectives
  • To predict the forecasting behavior.

  • To improve the prediction accuracy.

  • The proposed work uses the various climatic variables for the predictive analytics in the agriculture.

Contribution
  • The proposed work uses the various climatic variables for the predictive analytics in the agriculture.

  • 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.

Professional Ethics: We S-Logix would appreciate the students those who willingly contribute with atleast a line of thinking of their own while preparing the project with us. It is advised that the project given by us be considered only as a model project and be applied with confidence to contribute your own ideas through our expert guidance and enrich your knowledge.

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