A solution for M5 Forecasting - Uncertainty: Hybrid gradient boosting and autoregressive recurrent neural network for quantile estimation

Ernest Chiew and Shin Siang Choong

International Journal of Forecasting, 2022, vol. 38, issue 4, 1442-1447

Abstract: This paper presents our 13th place solution to the M5 Forecasting - Uncertainty challenge and compares it against GoodsForecast’s second-place solution. This challenge aims to estimate the median and eight other quantiles of various product sales in Walmart. Both solutions handle the predictions of median and other quantiles separately. Our solution hybridizes LightGBM and DeepAR in various ways for median and quantile estimation, based on the aggregation levels of the sales. Similarly, GoodsForecast’s solution also utilized a hybrid approach, i.e., LightGBM for point estimation and a Histogram algorithm for quantile estimation. In this paper, the differences between the two solutions and their results are highlighted. Despite our solution only taking 13th place in the challenge with the competition metric, it achieves the lowest average rank based on the multiple comparisons with the best (MCB) test which implies the most accurate forecasts in the majority of the series. It also indicates better performance at the product-store aggregation level which comprises 30,490 (71.2% of all) series compared to most teams.

Keywords: Time series; Estimation of distribution; DeepAR; Deep probabilistic forecasting; Deep learning; Sales forecast; Demand forecast (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:38:y:2022:i:4:p:1442-1447

DOI: 10.1016/j.ijforecast.2022.01.009

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