Main Reference PaperCommunication Network Time Series Prediction Algorithm Based on Big Data Method, Wireless Personal Communications, 2018 [Python/Hadoop].
  • A time series prediction algorithm is proposed on big data. The feature extraction is applied on time series data. The feature extraction components are periodic component, trend component, burst component and random error component. According to the feature extraction process, anomaly detection, the forecasting is predicted.

Description
  • A time series prediction algorithm is proposed on big data. The feature extraction is applied on time series data. The feature extraction components are periodic component, trend component, burst component and random error component. According to the feature extraction process, anomaly detection, the forecasting is predicted.

  • To analyze the forecasting from big data.

  • To improve the prediction accuracy.

Aim & Objectives
  • To analyze the forecasting from big data.

  • To improve the prediction accuracy.

  • To improve the prediction accuracy, in data preprocessing stage, the noisy data is removed.

Contribution
  • To improve the prediction accuracy, in data preprocessing stage, the noisy data is removed.

  • 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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