On predictive modeling of the twitter-based sales data using a new probabilistic model and machine learning methods
The term marketing refers to the various strategies employed by a company to enhance the visibility of its brands among potential consumers. Advertising serves as an effective channel for marketing efforts, allowing a company to showcase its products and services to the target audience. Numerous pro...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-02-01
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| Series: | Alexandria Engineering Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016824014996 |
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| Summary: | The term marketing refers to the various strategies employed by a company to enhance the visibility of its brands among potential consumers. Advertising serves as an effective channel for marketing efforts, allowing a company to showcase its products and services to the target audience. Numerous probability-oriented strategies have been suggested and carried out to investigate the effectiveness of advertising mechanisms. In accordance with this pivotal portion of the literature, we develop a new probabilistic model called a new cosine inverse Weibull (NCI-Weibull) distribution, which serves to analyze the sales related to advertising on the Twitter platform. The derivation of point estimators is accomplished for the new model. In addition, a simulation study is presented, which is based on the NCI-Weibull distribution. We further illustrate the applicability of the NCI-Weibull distribution through an analysis of Twitter-based sales data and comparing it with various other statistical models. Furthermore, we employ two machine learning techniques to forecast the sales, with a particular emphasis on 1-step and 3-step advance predictions. The statistical analysis indicates that multilayer perceptron (MLP) is superior in the field of short-term forecasting, whereas support vector regression (SVR) is more effective in the context of longer-term predictions. |
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| ISSN: | 1110-0168 |