A Systematic comparison of performance of Ridge, Lasso, Elastic net and Relaxed Elastic net when fitting high dimensional data for sales prediction
Date
2022
Authors
Muoki, Monica Mueni
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Journal ISSN
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Publisher
Strathmore University
Abstract
Forecasting or prediction is one of the most crucial aspects of planning for many companies. Data-driven decisions can only be as accurate as the prediction they are based on. Some of the decisions include production planning, inventory management, and various resource allocation. Sales information is really multi-dimensional, and as a result not easy to analyse. Our motivation is to reduce the high dimension of this information, select optimal contributing variables with the aim of making accurate and reliable sales predictions. The purpose of this study is to compare the performance of four restricted regressions. This involves looking at Ridge, Lasso, Relaxed net and Elastic net regressions and assessing their performance in prediction when dealing with high dimensional data. The proposed method will involve comparison of the four mentioned regularized techniques, citing their restrictions and evaluating their prediction model performance. We will also involve data simulation to test the different models. The simulations are done under different scenarios to present the reality of a market setting. Afterwards, we will select the best model and use it to fit our real sales dataset provided by one of the leading ECMCs in Kenya. On this basis, elastic net offered best predictions based. The evaluating metrics for this models are Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and R-Squared (R2). However, the desired model based on R2 kept shifting under different scenarios to Lasso, Ridge and Elastic net. The results indicated that the regularized approaches especially elastic net are capable of dealing with non-linearity and fluctuating dynamics in manufacturing industry while predicting electrical cable sales accurately.
Description
Submitted in partial fulfillment of the requirements for the degree of Master of Science in Statistical Science at Strathmore University