A comparative study of linear and nonlinear models for aggregate retail sales forecasting

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Comparison of direct and iterative artificial neural network forecast approaches in multi-periodic time series forecasting (2009, background): “Chu and Zhang (2003) compared the accuracy of various linear and nonlinear models for forecasting aggregate retail sales.”
A case study on a hybrid wind speed forecasting method using BP neural network (2011, background): “Although there are many similar forecasting approaches in which neural networks have been built on deseasonalised time series data, they have mainly been used to predict aggregate retail sales [8], temperature [7], streamflow [27], total production value and soft drink [25] and total new privately o”
Retail forecasting: Research and practice (2019, background): “PromoCast is another well-known promotion-event forecasting model which was developed by Cooper et al. (1999). They used a static cross-sectional regression analysis of SKU-store sales under a variety of promotion conditions, with store and chain specific historical performance information. Divakar ”
Performance of state space and ARIMA models for consumer retail sales forecasting (2015, background): “Zhang and Qi [9] and Kuvulmaz et al. [10] further investigated the use of ANNs in forecasting time series with strong trend and seasonality and conclude that the overall out-of-sample forecasting performance of ANNs, evaluated via RMSE, MAE and MAPE, is not better than ARIMA models in predicting ret”