Sales Forecasting with Partial Recurrent Neural Networks: Empirical Insights and Benchmarking Results

Partial Recurrent Neural Networks (PRNN) belong to the family of Artificial Neural Networks. Due to their specific architecture, PRNN are well-suited to forecast time series data. Their ability to outperform well-known statistical forecasting models has been demonstrated in some application domains. However, the potential of PRNN in business decision support and sales forecasting in particular has received relatively little attention. The paper strives to close this research gap. In particular, the paper provides a managerial introduction to PRNN and assesses their forecasting performance vis-à-vis challenging statistical benchmarks using real-world sales data. The sales time series are selected such that they encompass several characteristic patterns (e.g., Seasonality, trend, etc.) and differ in shape and length. Such heterogeneity is commonly encountered in sales forecasting and facilitates a holistic assessment of PRNN, and their potential to generate operationally accurate forecasts.