How to Learn from Others: Transfer Machine Learning with Additive Regression Models to Improve Sales Forecasting (2005.10698v1)

Abstract: In a variety of business situations, the introduction or improvement of machine learning approaches is impaired as these cannot draw on existing analytical models. However, in many cases similar problems may have already been solved elsewhere-but the accumulated analytical knowledge cannot be tapped to solve a new problem, e.g., because of privacy barriers. For the particular purpose of sales forecasting for similar entities, we propose a transfer machine learning approach based on additive regression models that lets new entities benefit from models of existing entities. We evaluate the approach on a rich, multi-year dataset of multiple restaurant branches. We differentiate the options to simply transfer models from one branch to another ("zero shot") or to transfer and adapt them. We analyze feasibility and performance against several forecasting benchmarks. The results show the potential of the approach to exploit the collectively available analytical knowledge. Thus, we contribute an approach that is generalizable beyond sales forecasting and the specific use case in particular. In addition, we demonstrate its feasibility for a typical use case as well as the potential for improving forecasting quality. These results should inform academia, as they help to leverage knowledge across various entities, and have immediate practical application in industry.

• The paper demonstrates that transfer learning with additive regression models can achieve competitive forecasting performance even in zero-shot scenarios.
• The study utilizes Prophet-based additive models to effectively capture trends and seasonality in multi-year restaurant sales data.
• Key implications include enhanced prediction accuracy, reduced data requirements, and scalable model transfer across similar business entities.

Overview of Transfer Machine Learning with Additive Regression Models for Sales Forecasting

This paper presents a methodology employing transfer machine learning for sales forecasting, leveraging additive regression models to facilitate knowledge transfer between similar entities. The research addresses a significant challenge in machine learning applications: the capacity to draw on accumulated analytical knowledge without breaching privacy barriers prevalent in business environments. The researchers propose a novel approach that allows new entities to benefit from pre-existing models of similar entities, thus overcoming the limitations of a lack of initial data and constrained data exchange due to privacy concerns.

The core contribution lies in demonstrating a practical, data-efficient means to accomplish sales forecasts in restaurant chains using transfer learning principles. The experiments conducted highlight the potential of using additive regression models to achieve competitive performance, even when models are transferred between different entities (or branches) without adaptation ("zero shot"), as well as when these models are further adapted to fit the target data.

Methodology and Experimentation

The paper employs an additive regression model implemented via the Prophet library. By leveraging time series data, the method captures trends and seasonality, providing a dependable foundation for forecasting sales. The researchers conducted robust evaluations on a dataset featuring multiple years of sales records from various branches within two restaurant chains. The investigative thrust is structured around three research questions:

1. The ability to forecast sales for individual branches based on their historical data.
2. The feasibility of transferring machine learning models from one branch to another without any target branch historical data ("zero shot").
3. The effectiveness of adapted transfer learning, where models pre-trained on source branch data are adapted using limited target branch historical data.

In addressing these questions, the authors lay out a clear framework for comparison. Baseline methods include the seasonal naïve approach and models trained on varying configurations of available data.

Results and Implications

The findings elucidate several critical points:

• The models developed in isolation for individual branches frequently outperform baseline methods and demonstrate increased effectiveness with more extensive training data.
• "Zero shot" transfer learning yields performance outcomes that, even without target data adaptation, are superior in some instances to conventional models utilizing historical target data.
• Adapted transfer learning showcases significant improvements in performance, often surpassing both baseline models and isolated learning approaches.

These outcomes have essential theoretical implications, suggesting that successful model generalization across similar entities is achievable without necessitating the full dataset for each entity. Practically, this translates into enhanced forecasting precision with reduced data collection requirements and circumvents privacy-related data exchange issues.

Future Directions

While the paper provides a sturdy foundation for generalized model development, it also opens avenues for further exploration. Future research efforts could focus on automating model adaptation processes, exploring various adaptation strategies, and assessing model transferability based on entity characteristics. Moreover, integrating additional external data sources—such as weather or event-based information—might refine predictions further.

This exploration of transfer learning for sales forecasting with shallow learning methodologies sets a precedent for broader applications of model transferability in machine learning, offering valuable insights into strategic model deployment across disparate organizational contexts.