# Construction of Ensemble Learning Model for Home Appliance Demand Forecasting
## 1 Introduction
[25] proposed a combined granulation-based Long Short-Term Memory (LSTM)-Random Forest (RF) model for accurately predicting Sea Surface Temperature (SST), which was capable of adequately capturing the feature dependencies involved in the SST series' fluctuations, reducing cumulative error during the iteration process, and extending the prediction range. However, while a number of studies have presented in-depth evaluations of the demand forecasting problem, relatively few studies have explored it considering products in the appliance industry, and the methodologies continue to have defects and limits. According to the cited literature, some approaches may rely too much on the features of a particular dataset, resulting in poor generalization across different datasets. In addition, there are too many influencing factors for home appliances to consider, such as products, seasons, festivals, and environments [26; 27], whereas technological advancements have resulted in an increase in purchasing channels and an overabundance of choices for consumers, rather than a lack of available products [28]. To address these issues, this paper proposes the LSTM-RF-XGBoost model. Based on [17; 18; 19], the selection procedure for the first layer of a single model was determined. Given the typical cyclical and seasonal challenges in demand forecasting for home appliances, we propose incorporating the LSTM model, which effectively identifies and predicts cyclical fluctuations by capturing long-term dependencies in time series data. To address the large fluctuations in demand and enhance the accuracy and robustness of predictions, we also introduce the Random Forest (RF) and XGBoost models. The RF model, by integrating multiple decision trees, mitigates the risk of overfitting and ensures robustness and stability across different datasets. Simultaneously, XGBoost's strong capability to handle noise and outliers allows for precise forecasting, even under conditions of significant data fluctuations. Building on these models, we conduct feature interaction and feature selection to further improve the models' predictive performance. We also choose more than 100,000 data points from a home appliance manufacturing industry in China from January 2018 to December 2022 to support the study and verify the validity and accuracy of this model. Compared with previous studies, the contributions and novelties of our research are summarized below. First, we present a hybrid LSTM-RF-XGBoost model for forecasting home appliance demand. We combine the strengths of various models and further refine them to capture the complex dynamics of appliance demand through feature interaction and filtering, enabling us to account for the multiple factors that influence consumer demand for home appliances. Second, we create four distinct feature clusters: time, product attributes, target volume trends, and environmental factors, and our study offers the prospect of capturing a broader range of elements influencing the demand for appliance product analysis. Our study offers analytical suggestions for home appliance products with a wide range of influencing features. Finally, we discover that the five features of shipments, total sales, e-commerce festivals, seasons, and productions have the greatest impact on air conditioner demand. This result can provide analytical ideas for home appliances with a clearly cyclical demand profile, such as air conditioners, as well as fresh insights into demand management in the home appliance business. The rest of this paper is arranged as follows: Section 2 introduces the demand prediction models for this paper. Section 3 analyzes the data distribution and screens the effective features and experimental process used for the basis of this paper. Section 4 offers a summary of our findings and conclusions, along with suggestions for future research directions.