A novel prediction algorithm for multivariate data sets

Pinki Sagar,Prinima Gupta,Rohit Tanwar

doi:10.31181/dmame210402215s

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.

Highlights

Regression techniques come under the category of supervised learning methods in which the existing training data sets can be used as guidance and to supervise thePinki et al./Decis
In this paper, a Multivariate Item Prediction Algorithm (MIPA) algorithm is compared with MLR because it deals with multiple independent variables
As compared with MLR, it has been evaluated that Root Mean Square Error (RMSE)'s are low for the MIPA algorithm

Summary

Introduction

Regression techniques come under the category of supervised learning methods in which the existing training data sets can be used as guidance and to supervise thePinki et al./Decis. Regression techniques come under the category of supervised learning methods in which the existing training data sets can be used as guidance and to supervise the. New values are predicted for future analysis, which will be calculated based on historical or previous data sets. Linear Regression fits a straight line and it has two components b0 (intercept) and coefficient b1 and one predictor termed as the independent variable. In today’s scenarios, data sets are maintained with multiple attributes and it requires so much processing time and costs for prediction. The cost of preprocessing and maintenance is depending on the type of data sets but at the time of analysis, it is not necessary to consider all attributes. The regression coefficient b1 is the average change in the dependent variable ‘y’ for a per-unit change in the independent variable ‘y’

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Conclusion