Robust recurrent network model for intermittent time-series forecasting

Yunho Jeon and Sihyeon Seong

International Journal of Forecasting, 2022, vol. 38, issue 4, 1415-1425

Abstract: This paper describes a deep-learning-based time-series forecasting method that was ranked third in the accuracy challenge of the M5 competition. We solved the problem using a deep-learning approach based on DeepAR, which is an auto-regressive recurrent network model conditioned on historical inputs. To address the intermittent and irregular characteristics of sales demand, we modified the training procedure of DeepAR; instead of using actual values for the historical inputs, our model uses values sampled from a trained distribution and feeds them to the network as past values. We obtained the final result using an ensemble of multiple models to make a robust and stable prediction. To appropriately select a model for the ensemble, each model was evaluated using the average weighted root mean squared scaled error, calculated for all levels of a wide range of past periods.

Keywords: M5 accuracy competition; Time-series forecasting; DeepAR; Tweedie; Ensemble; Model selection (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:38:y:2022:i:4:p:1415-1425

DOI: 10.1016/j.ijforecast.2021.07.004

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