A novel prediction algorithm for multivariate data sets

  • Pinki Sagar Computer Science and Technology, Manav Rachna University, Haryana, India
  • Prinima Gupta Computer Science and Technology, Manav Rachna University, Haryana, India
  • Rohit Tanwar School of Computer Science, University of Petroleum & Energy Studies, Dehradun Uttarakhand, India
Keywords: Coefficient of determination, Mean square error, Actual means, Multiple Linear Regression (MLR), Root Mean Square Error (RMSE), Mean Square Error (MSE)

Abstract

Regression analysis is a statistical technique that is most commonly used for forecasting. Data sets are becoming very large due to continuous transactions in today's high-paced world. The data is difficult to manage and interpret. All the independent variables can’t be considered for the prediction because it costs high for maintenance of the data set. A novel algorithm for prediction has been implemented in this paper. Its emphasis is on extraction of efficient independent variables from various variables of the data set. The selection of variables is based on Mean Square Errors (MSE) as well as on the coefficient of determination r2p, after that the final prediction equation for the algorithm is framed on the basis of deviation of actual mean. This is a statistical based prediction algorithm which is used to evaluate the prediction based on four parameters:  Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and residuals. This algorithm has been implemented for a multivariate data set with low maintenance costs, preprocessing costs, lower root mean square error and residuals. For one dimensional, two-dimensional, frequent stream data, time series data and continuous data, the proposed prediction algorithm can also be used. The impact of this algorithm is to enhance the accuracy rate of forecasting and minimized average error rate.

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Published
2021-07-15
How to Cite
Sagar, P., Gupta, P., & Tanwar, R. (2021). A novel prediction algorithm for multivariate data sets. Decision Making: Applications in Management and Engineering, 4(2), 225-240. https://doi.org/10.31181/dmame210402215s