Abstract
Accurately forecasting sales is a significant challenge faced by almost all companies. In particular, most products have short lifecycles without the accumulation of historical sales data. Existing methods either fail to capture the context-specific, irregular trends or to integrate as much information as is available in the face of a data scarcity problem. To address these challenges, we propose a new model, called F-TADA, i.e., future-aware TADA, which is derived from trend alignment with dual-attention multi-task recurrent neural networks (TADA). We utilize two real-world supply chain sales data sets to verify our algorithm’s performance and effectiveness on both long and short lifecycles. The experimental results show that the accuracy of the F-TADA is better than the original model. Our model’s performance could be further improved, however, by appropriately increasing the length of the windows in the decoding stage. Finally, we develop a sales data prediction and analysis decision-making system, which can offer intelligent sales guidance to enterprises.
Highlights
Accurate sales forecasting is crucial for supply chain management
Sales prediction can be formulated as a time series forecasting problem, which is usually solved by the autoregressive model (AR) [1] or autoregressive moving average model (ARMA) [2,3]
The AR and ARMA models are suitable for stationary time series, but most time series data are non-stationary, so various linear and non-linear time series models [4], namely autoregressive integrated moving average (ARIMA) [5], seasonal-ARIMA, the seasonally decomposed autoregressive (STL-ARIMA) algorithm [6], the autoregressive conditional heteroscedasticity model (ARCH), and generalized autoregressive conditional heteroskedasticity (GARCH), have come into being
Summary
Accurate sales forecasting is crucial for supply chain management. Overestimation or underestimation can affect inventory, cash flow, business reputation, and profit. It has attracted attention from both academic and industrial worlds. Sales prediction can be formulated as a time series forecasting problem, which is usually solved by the autoregressive model (AR) [1] or autoregressive moving average model (ARMA) [2,3]. Autoregressionmethods that can model cointegration (autoregressive distributed lag (ARDL)) [7] or estimate covariance functions (stochastic autoregressive moving average (ARMA)
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