Improved Sales Forecasting Using Trend and Seasonality Decomposition with LightGBM

EasyChair Preprint 10926

Abstract

Retail sales forecasting presents a significant challenge for large retailers such as Walmart and Amazon, due to the vast assortment of products, geographical location heterogeneity, seasonality, and external factors including weather, local economic conditions, and geopolitical events. Various methods have been employed to tackle this challenge, including traditional time series models, machine learning models, and neural network mechanisms, but the difficulty persists. Categorizing data into relevant groups has been shown to improve sales forecast accuracy as time series from different categories may exhibit distinct patterns. In this paper, we propose a new measure to indicate the unique impacts of the trend and seasonality components on a time series and suggest grouping time series based on this measure. We apply this approach to Walmart sales data from 01/29/2011 to 05/22/2016 and generate sales forecasts from 05/23/2016 to 06/19/2016. Our experiments show that the proposed strategy can achieve improved accuracy. Furthermore, we present a robust pipeline for conducting retail sales forecasting.

Keyphrases: LightGBM, Prophet model, Sales Forecasting, Trend and Seasonality Decomposition, Walmart

@booklet{EasyChair:10926,
  author    = {Tong Zhou},
  title     = {Improved Sales Forecasting Using Trend and Seasonality Decomposition with LightGBM},
  howpublished = {EasyChair Preprint 10926},
  year      = {EasyChair, 2023}}