Multivariate Sales Forecasting Using Gated Recurrent Unit Network Model

Document Type : Research Paper

Authors

1 Department of Transport and Logistics Management, Faculty of Engineering, University of Moratuwa, Katubedda 10400, Sri Lanka.

2 Center for Supply Chain, Operations and Logistics Optimization, University of Moratuwa, Katubedda 10400, Sri Lanka

Abstract

Market forecasting is an integral part of supply chain management. Machine learning models have turned a new page in predictive analysis and helped organizations achieve improved accuracy. This paper focuses on creating a Gated Recurrent Unit (GRU) model to predict sales for multiple stores as a multivariate time series. GRUs are a variation of Recurrent Neural Networks (RNNs) used to sequence modelling tasks. The dataset used to create the model contains the unit sales of 3,049 SKUs sold in 10 stores. The sales data from the 3049 SKUs were grouped into the 7 departments to use as input to the model. A Vector Autoregression (VAR) and LightGBM models were used to compare the GRU model predictions. Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) were used to compare the 2 models. The mean MAPE values for forecasts of the GRU, VAR, and LightGBM models were 13.77%, 14.87%, and 14.24% respectively, while MAE values were 68 Units, 72 Units, and 69 Units Respectively. The study reveals that the GRU model provides more accuracy for multivariate sales forecasting due to its ability to learn hidden patterns automatically and handle time mechanisms such as trends and seasonality.

Keywords

References

References
Azubuike, I., & Kosemoni, O. (2017). A Comparison of Univariate and Multivariate Time Series Approaches to Modeling Currency Exchange Rate. British Journal of Mathematics & Computer Science, 21(4), 1–17. https://doi.org/10.9734/bjmcs/2017/30733
Ahaggach, H., Abrouk, L., & Lebon, E. (2024). Systematic Mapping Study of Sales Forecasting: Methods, Trends, and Future Directions. Forecasting, 6(3), 502-532. https://doi.org/10.3390/FORECAST6030028
Arachchige, A., Sugathadasa, R., Herath, O., & Thibbotuwawa, A. (2021). Artificial Neural Network Based
Demand Forecasting Integrated With Federal Funds Rate. Applied Computer Science, 17(4), 34–44.
https://doi.org/10.23743/acs-2021-27
Bandara, K., Shi, P., Bergmeir, C., Hewamalage, H., Tran, Q., & Seaman, B. (2019). Sales Demand Forecast in E-commerce using a Long Short-Term Memory Neural Network Methodology. Springer International Publishing. http://arxiv.org/abs/1901.04028
Chang, Y.-Y., Sun, F.-Y., Wu, Y.-H., & Lin, S.-D. (2018). A Memory-Network Based Solution for Multivariate Time-Series Forecasting. ArXiv. http://arxiv.org/abs/1809.02105
Chen, Z., Ma, M., Li, T., Wang, H., & Li, C. (2023). Long sequence time-series forecasting with deep learning: A survey. Information Fusion, 97, 101819. https://doi.org/10.1016/J.INFFUS.2023.101819
Cochran, J. J., Cox, L. A., Keskinocak, P., Kharoufeh, J. P., Smith, J. C., Wang, S., & Chaovalitwongse, W. A. (2011). Evaluating and Comparing Forecasting Models. In Wiley Encyclopedia of Operations Research and Management Science. John Wiley & Sons, Inc. https://doi.org/10.1002/9780470400531.eorms0307
Crone, S. F., Hibon, M., & Nikolopoulos, K. (2011). Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction. International Journal of Forecasting, 27(3), 635–660. https://doi.org/10.1016/j.ijforecast.2011.04.001
Davydenko, A., & Fildes, R. (2013). Measuring Forecasting Accuracy: The Case Of Judgmental Adjustments To Sku-Level Demand Forecasts. International Journal of Forecasting, 29(3), 510–522. https://doi.org/10.1016/j.ijforecast.2012.09.002
Eglite, L., & Birzniece, I. (2022). Retail Sales Forecasting Using Deep Learning: Systematic Literature Review. Complex Systems Informatics and Modeling Quarterly, 2022(30). https://doi.org/10.7250/csimq.2022-30.03
Forslund, H., & Jonsson, P. (2007). The impact of forecast information quality on supply chain performance. International Journal of Operations and Production Management, 27(1), 90–107. https://doi.org/10.1108/01443570710714556
Gilbert, K. (2005). An ARIMA supply chain model. In Management Science (Vol. 51, Issue 2, pp. 305–310). https://doi.org/10.1287/mnsc.1040.0308
He, Q. Q., Wu, C., & Si, Y. W. (2022). LSTM with particle Swam optimization for sales forecasting. Electronic Commerce Research and Applications, 51. https://doi.org/10.1016/j.elerap.2022.101118
Helmini, S., Jihan, N., Jayasinghe, M., & Perera, S. (2019). Sales forecasting using multivariate long short term memory network models. https://doi.org/10.7287/peerj.preprints.27712v1
Hewage, H. C., & Perera, H. N. (2021). Comparing Statistical and Machine Learning Methods for Sales Forecasting during the Post-promotional Period. 2021 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2021. https://doi.org/10.1109/IEEM50564.2021.9672954
Hewage, H. C., Perera, H. N., & De Baets, S. (2022). Forecast adjustments during post-promotional periods. European Journal of Operational Research, 300(2). https://doi.org/10.1016/j.ejor.2021.07.057
Hewamalage, H., Bergmeir, C., & Bandara, K. (2020). Recurrent Neural Networks for Time Series Forecasting: Current status and future directions. International Journal of Forecasting. https://doi.org/10.1016/j.ijforecast.2020.06.008
Hyndman, R. J. (2020). A brief history of forecasting competitions. International Journal of Forecasting, 36(1), 7–14. https://doi.org/10.1016/j.ijforecast.2019.03.015
Kolassa, S. (2016). Evaluating predictive count data distributions in retail sales forecasting. International Journal of Forecasting, 32(3), 788–803. https://doi.org/10.1016/j.ijforecast.2015.12.004
Lai, K. K. (2006). An Integrated Data Preparation Scheme for Neural Network Data Analysis. IEEE Transactions on Knowledge and Data Engineering, 18(2), 217–230. https://doi.org/10.1109/TKDE.2006.22
Li, Z., Han, J., & Song, Y. (2020). On the forecasting of high‐frequency financial time series based on ARIMA model improved by deep learning. Journal of Forecasting, 39(7), 1081–1097. https://doi.org/10.1002/for.2677
Lipton, Z. C., Berkowitz, J., & Elkan, C. (2015). A Critical Review of Recurrent Neural Networks for Sequence Learning. http://arxiv.org/abs/1506.00019
Makridakis, S., Spiliotis, E., & Assimakopoulos, V. (2020). The M4 Competition: 100,000 time series and 61 forecasting methods. International Journal of Forecasting, 36, 54–74. https://doi.org/10.1016/j.ijforecast.2019.04.014
Obaidur Rahman, M., Sabir Hossain, M., Shafiul Alam Forhad, M., Kamal Hossen, M., & Junaid, T.-S. (2019). Predicting Prices of Stock Market using Gated Recurrent Units (GRUs) Neural Networks. In IJCSNS International Journal of Computer Science and Network Security (Vol. 19, Issue 1). https://www.researchgate.net/publication/331385031
Predić, B., Jovanovic, L., Simic, V., Bacanin, N., Zivkovic, M., Spalevic, P., Budimirovic, N., & Dobrojevic, M. (2024). Cloud-load forecasting via decomposition-aided attention recurrent neural network tuned by modified particle swarm optimization. Complex and Intelligent Systems, 10(2), 2249–2269. https://doi.org/10.1007/S40747-023-01265-3/FIGURES/11
Perera, H. N., Hurley, J., Fahimnia, B., & Reisi, M. (2019). The human factor in supply chain forecasting: A systematic review. European Journal of Operational Research, 274(2), 574–600. https://doi.org/10.1016/j.ejor.2018.10.028
Qin, Z., Yang, S., & Zhong, Y. (2024). Hierarchically Gated Recurrent Neural Network for Sequence Modeling. Retrieved August 19, from https://github.com/OpenNLPLab/HGRN
Šestanović, T., & Arnerić, J. (2021). Neural network structure identification in inflation forecasting. Journal of Forecasting, 40(1), 62–79. https://doi.org/10.1002/for.2698
Wang, P., Gurmani, S. H., Tao, Z., Liu, J., & Chen, H. (2024). Interval time series forecasting: A systematic literature review. Journal of Forecasting, 43(2), 249–285. https://doi.org/10.1002/FOR.3024
Yang, K., & Shahabi, C. (2005). On the stationarity of multivariate time series for correlation-based data analysis. Proceedings - IEEE International Conference on Data Mining, ICDM, 805–808. https://doi.org/10.1109/ICDM.2005.109
Zhang, G., Eddy Patuwo, B., & Y. Hu, M. (1998). Forecasting with artificial neural networks: The state of the art. International Journal of Forecasting, 14(1), 35–62. https://doi.org/10.1016/S0169-2070(97)00044-7
International Journal of Supply and Operations Management
Volume 11, Issue 4
November 2024
Pages 390-416

Files

Share

How to cite

Statistics