An Exponential Factorization Machine with Percentage Error Minimization to Retail Sales Forecasting

09/22/2020
by   Chongshou Li, et al.
0

This paper proposes a new approach to sales forecasting for new products with long lead time but short product life cycle. These SKUs are usually sold for one season only, without any replenishments. An exponential factorization machine (EFM) sales forecast model is developed to solve this problem which not only considers SKU attributes, but also pairwise interactions. The EFM model is significantly different from the original Factorization Machines (FM) from two-fold: (1) the attribute-level formulation for explanatory variables and (2) exponential formulation for the positive response variable. The attribute-level formation excludes infeasible intra-attribute interactions and results in more efficient feature engineering comparing with the conventional one-hot encoding, while the exponential formulation is demonstrated more effective than the log-transformation for the positive but not skewed distributed responses. In order to estimate the parameters, percentage error squares (PES) and error squares (ES) are minimized by a proposed adaptive batch gradient descent method over the training set. Real-world data provided by a footwear retailer in Singapore is used for testing the proposed approach. The forecasting performance in terms of both mean absolute percentage error (MAPE) and mean absolute error (MAE) compares favourably with not only off-the-shelf models but also results reported by extant sales and demand forecasting studies. The effectiveness of the proposed approach is also demonstrated by two external public datasets. Moreover, we prove the theoretical relationships between PES and ES minimization, and present an important property of the PES minimization for regression models; that it trains models to underestimate data. This property fits the situation of sales forecasting where unit-holding cost is much greater than the unit-shortage cost.

READ FULL TEXT

page 1

page 2

page 3

page 4

06/12/2015

Using the Mean Absolute Percentage Error for Regression Models

We study in this paper the consequences of using the Mean Absolute Perce...
09/08/2015

Empirical risk minimization is consistent with the mean absolute percentage error

We study in this paper the consequences of using the Mean Absolute Perce...
05/09/2016

Mean Absolute Percentage Error for regression models

We study in this paper the consequences of using the Mean Absolute Perce...
08/17/2021

Memory-Efficient Factorization Machines via Binarizing both Data and Model Coefficients

Factorization Machines (FM), a general predictor that can efficiently mo...
07/25/2022

Deep Learning for Forecasting the Energy Consumption in Public Buildings

In this paper we propose a Long Short-Term Memory Network based method t...
12/08/2021

Regularization methods for the short-term forecasting of the Italian electric load

The problem of forecasting the whole 24 profile of the Italian electric ...
01/05/2018

Multiple changepoint detection for periodic autoregressive models with an application to river flow analysis

In river flow analysis and forecasting there are some key elements to co...