Affiliation:
1. University of São Paulo
Abstract
Abstract
This work aims to predict demand for orders in the expanded center of S˜ao Paulo, Brazil, using the Spatial Autoregressive Model (SAR) and the Artificial Neural Network - Multilayer Perceptron Model (MLP), based on real data from a Brazilian online food delivery company. Accurately forecasting demand by geographical area is crucial for efficient logistics planning in the company. To address this, we proposed an approach that uses an augmented train matrix to incorporate order information from neighboring areas of the first order, allowing the neural network model to identify spatial autocorrelation in the data. The performance of both models was evaluated using Root Mean Squared Error (RMSE) and the coefficient of determination (R2). In the simulations, both of the models achieved satisfactory performance compared to the historical mean. The MLP model, when best tuned, outperformed the SAR model in both RMSE and R2 coefficient.
Publisher
Research Square Platform LLC
Reference68 articles.
1. Hamed, Khaled H and Rao, A Ramachandra (1998) A modified Mann-Kendall trend test for autocorrelated data. Journal of hydrology 204(1-4): 182--196 Elsevier
2. Taylor, James W (2003) Short-term electricity demand forecasting using double seasonal exponential smoothing. Journal of the Operational Research Society 54(8): 799--805 Taylor & Francis
3. Rey, Sergio J and Anselin, Luc PySAL: A Python library of spatial analytical methods. Handbook of applied spatial analysis, Springer, 2010, 175--193
4. Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V. and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P. and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E. (2011) Scikit-learn: Machine Learning in {P}ython. Journal of Machine Learning Research 12: 2825--2830
5. Hastie, Trevor and Tibshirani, Robert and Friedman, Jerome (2009) The elements of statistical learning: data mining, inference, and prediction. Springer Science & Business Media