<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The aim of this study is to forecast the revenue of a seller taking part in an online e-commerce marketplace by using hybrid intelligent methods to help the seller build a solid financial plan. For this purpose, three different approaches are applied in order to accurately forecast the revenue. In the first approach, after applying simple preprocessing steps on the dataset, forecast models are developed with Random Forest (RF). In the second approach, Isolation Forest (IF) is used to detect outliers on the dataset, and minimum Redundancy Maximum Relevance (mRMR) is utilized to select the features that affect the quality of revenue forecast, correctly. In the last approach, a feature selection process is performed first and then the Density-Based Spatial Clustering and Application with Noise (DBSCAN) is used to cluster the dataset. After these processes are carried out, forecast models are developed with RF. The dataset used includes the daily revenue of a seller with several other features. Mean Absolute Percent Error (MAPE) is used for evaluating the performance of the forecast models.</p>
</abstract>

The aim of this study is to forecast the revenue of a seller taking part in an online e-commerce marketplace by using hybrid intelligent methods to help the seller build a solid financial plan. For this purpose, three different approaches are applied in order to accurately forecast the revenue. In the first approach, after applying simple preprocessing steps on the dataset, forecast models are developed with Random Forest (RF). In the second approach, Isolation Forest (IF) is used to detect outliers on the dataset, and minimum Redundancy Maximum Relevance (mRMR) is utilized to select the features that affect the quality of revenue forecast, correctly. In the last approach, a feature selection process is performed first and then the Density-Based Spatial Clustering and Application with Noise (DBSCAN) is used to cluster the dataset. After these processes are carried out, forecast models are developed with RF. The dataset used includes the daily revenue of a seller with several other features. Mean Absolute Percent Error (MAPE) is used for evaluating the performance of the forecast models.

Revenue forecast models using hybrid intelligent methods

International Journal of Mathematics and Computer in Engineering

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Revenue forecast models using hybrid intelligent methods"}

The aim of this study is to forecast the revenue of a seller taking part in an online e-commerce marketplace by using hybrid intelligent methods to help the...

Revenue forecast models using hybrid intelligent methods

Article Category: Original Study

Pubblicato online: 31 ott 2023

Pagine: 117 - 124

Ricevuto: 14 giu 2023

Accettato: 22 lug 2023

DOI: https://doi.org/10.2478/ijmce-2024-0009

Parole chiave
Revenue forecasting, machine learning, hybrid methods, marketplace

© 2024 Gizem Topaloğlu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Revenue forecast models using hybrid intelligent methods

Article Category: Original Study

Pubblicato online: 31 ott 2023

Pagine: 117 - 124

Ricevuto: 14 giu 2023

Accettato: 22 lug 2023

DOI: https://doi.org/10.2478/ijmce-2024-0009

Parole chiaveRevenue forecasting, machine learning, hybrid methods, marketplace

© 2024 Gizem Topaloğlu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Parole chiave
Revenue forecasting, machine learning, hybrid methods, marketplace