Revenue forecast models using hybrid intelligent methods

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 forecast the revenue, accurately. 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 correctly that affect the quality of revenue forecast. In the last approach, 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. These results show that the average MAPE of the third approach is 17.40% lower than that of the first approach, and 10.15% lower than that of the second approach.

Service Reliability Based on Fault Prediction and Container Migration in Edge Computing

“…Omar et al (2023) applied LSTM and GPT2 to classify software code for potential vulnerabilities [21]. Topaloglu et al (2023) used machine-learning algorithms on the price forecasting on e-commerce data [22]. To address this gap in the literature, in this study, the container migration timing is determined by fault prediction in the first step.…”