Retail Demand Forecasting: A Comparative Analysis of Deep Neural Networks and the Proposal of LSTMixer, a Linear Model Extension - Georgios Theodoridis; Athanasios Tsadiras (Information)
Accurate retail demand forecasting is integral to the operational efficiency of any retail business. As demand is described over time, the prediction of demand is a time-series forecasting problem which may be addressed in a univariate manner, via statistical methods and simplistic machine learning approaches, or in a multivariate fashion using generic deep learning forecasters that are well-established in other fields. This study analyzes, optimizes, trains and tests such forecasters, namely the Temporal Fusion Transformer and the Temporal Convolutional Network, alongside the recently proposed Time-Series Mixer, to accurately forecast retail demand given a dataset of historical sales in 45 stores with their accompanied features. Moreover, the present work proposes a novel extension of the Time-Series Mixer architecture, the LSTMixer, which utilizes an additional Long Short-Term Memory block to achieve better forecasts. The results indicate that the proposed LSTMixer model is the better predictor, whilst all the other aforementioned models outperform the common statistical and machine learning methods. An ablation test is also performed to ensure that the extension within the LSTMixer design is responsible for the improved results. The findings promote the use of deep learning models for retail demand forecasting problems and establish LSTMixer as a viable and efficient option.