Comparison of selection and combination strategies for demand forecasting methods
Saymon Galvão Bandeira; Symone Gomes Soares Alcalá; Roberto Oliveira Vita; Talles Marcelo Gonçalves de Andrade Barbosa
Abstract
Keywords
References
Adya, M., Collopy, F., Armstrong, J. S., & Kennedy, M. (2001). Automatic identification of time series features for rule-based forecasting.
Armstrong, J. S. (Ed.) (2001).
Babai, M. Z., Dallery, Y., Boubaker, S., & Kalai, R. (2019). A new method to forecast intermittent demand in the presence of inventory obsolescence.
Barrow, D. K., & Kourentzes, N. (2016). Distributions of forecasting errors of forecast combinations: Implications for inventory management.
Choi, J. Y., & Lee, B. (2018). Combining LSTM Network Ensemble via Adaptive Weighting for Improved Time Series Forecasting.
Collopy, F., & Armstrong, J. S. (1992). Rule-based forecasting: development and validation of an expert systems approach to combining time series extrapolations.
Croston, J. D. (1972). Forecasting and stock control for intermittent demands.
Fildes, R., & Petropoulos, F. (2015). Simple versus complex selection rules for forecasting many time series.
Frank, A. G., Dalenogare, L. S., & Ayala, N. F. (2019). Industry 4.0 technologies: implementation patterns in manufacturing companies.
Franses, P. H., & Legerstee, R. (2011). Combining SKU-level sales forecasts from models and experts.
Guo, F., Diao, J., Zhao, Q., Wang, D., & Sun, Q. (2017). A double-level combination approach for demand forecasting of repairable airplane spare parts based on turnover data.
Heinecke, G., Syntetos, A. A., & Wang, W. (2013). Forecasting-based SKU classification.
Holt, C. C. (2004). Forecasting seasonals and trends by exponentially weighted moving averages.
Hyndman, R. J., & Athanasopoulos, G. (2018).
Hyndman, R. J., & Khandakar, Y. (2008). Automatic time series forecasting: the forecast package for R.
Hyndman, R. J., & Koehler, A. B. (2006). Another look at measures of forecast accuracy.
Kourentzes, N. (2013). Intermittent demand forecasts with neural networks.
Kourentzes, N., Barrow, D., & Petropoulos, F. (2019). Another look at forecast selection and combination: evidence from forecast pooling.
M3-Competition. (2020).
Makridakis, S., & Hibon, M. (2000). The M3-competition: results, conclusions and implications.
Makridakis, S., Spiliotis, E., & Assimakopoulos, V. (2018). The M4 Competition: Results, findings, conclusion and way forward.
Moon, S., Simpson, A., & Hicks, C. (2013). The development of a classification model for predicting the performance of forecasting methods for naval spare parts demand.
Petropoulos, F., Kourentzes, N., Nikolopoulos, K., & Siemsen, E. (2018). Judgmental selection of forecasting models.
Qi, M., & Zhang, G. P. (2001). An investigation of model selection criteria for neural network time series forecasting.
Rego, J. R., & Mesquita, M. A. (2015). Demand forecasting and inventory control: a simulation study on automotive spare parts.
Seabold, S., & Perktold, J. (2010). Statsmodels: econometric and statistical modeling with python. In
Soares, S., Antunes, C., & Araújo, R. (2012). A genetic algorithm for designing neural network ensembles. In
Statsmodels. (2020).
Syntetos, A. A., Boylan, J. E., & Croston, J. D. (2005). On the categorization of demand patterns.
Teunter, R. H., & Duncan, L. (2009). Forecasting intermittent demand: a comparative study.
Wang, X., & Petropoulos, F. (2016). To select or to combine? The inventory performance of model and expert forecasts.
Yu, Y., Choi, T.-M., & Hui, C.-L. (2011). An intelligent fast sales forecasting model for fashion products.
Submitted date:
01/31/2020
Accepted date:
08/10/2020
Facebook
Google+
Twitter
LinkedIn
Mendeley
StumbleUpon
CiteULike
Reddit
Email