## 1 Introduction  
Forecasting remains at the forefront of decision making [1, 2], especially in the field of supply chain management where accurate predictions of demand and inventory levels can have a significant impact on business operations and profitability [3, 4]. Forecasting methodologies and algorithms to predict more accurate results have been developed throughout the ages [5]. In retail, rapid changes to the business environment, shorter planning horizons, lower profit margins, and customer service issues make forecasting more complex [6]. In addition, large numbers of product types compel businesses to adopt individual models for specific groups of products, covering linear to complex nonlinear patterns [7]. Global uncertainty and the plethora of complexities in the retail industry make forecasting accuracy one of the main priorities in this field. Recently, researchers have utilized advanced methods of forecasting, such as deep neural networks, like long short-term memory (LSTM) or ensemble learning, to increase accuracy. Ensemble techniques combine different algorithms into an individual method, where each algorithm could be more sensitive under varying conditions [8]. Combined forecasts or \"ensemble forecasts\" have historical roots in aggregating individual forecasts and are not a recent development. However, using them in machine learning modelshas recently gained increasing popularity in the field of applied artificial intelligence in business. More than 15% of forecasting research in 2021 mentioned combined machine learning models [9]. Numerous combination and ensemble techniques have been developed to improve forecasting accuracy. Among the different machine learning models, tree-based methods dominate in both accuracy and uncertainty handling [10]. Compared to other machine learning algorithms, tree-based models have relatively low requirements for data preparation tasks, like feature scaling [11]. Extra tree regression (ETR), which is a type of tree-based ensemble model, has gained popularity in predictive research due to its ability to learn faster with smaller input dimensions [12]. Ensemble forecasting is also utilized in the retail sector to overcome uncertainties in data, parameters, and models and to decrease the risks of relying on a single best model. However, the usage of ensemble models in retail prediction has not been widely investigated in the research [7]. To address this research gap, this paper aims to perform tree-based ensemble demand forecasting alongside deep learning with LSTM networks. To fulfill this objective, this study utilizes real-life data from a large-scale research project in retail supply chain management, comprising over 5.2 million records. More precisely, we utilize data from a prominent Austrian retailer, including daily demand data for over 330 products. The retail company has been a partner of a 4-year research project focusing on improving demand prediction for perishable food in grocery retail by means of advanced analytics. To compare the results of both approaches and determine the most effective one, the methodology of this paper builds on an acknowledged set of model evaluation criteria. The contributions of our paper that distinguish our work from previous studies can be summarized as follows:  
* Feature diversity: Our study goes beyond historical demand data by incorporating a wide array of diverse features, including price and external factors, such as weather and COVID-19-related data. This enriched dataset enhances our understanding of demand behavior;
* Real-world applicability: We utilize a substantial dataset from a prominent supermarket, demonstrating the real-world applicability of our findings.