A Universality–Distinction Mechanism-Based Multi-Step Sales Forecasting for Sales Prediction and Inventory Optimization
Sales forecasting is a highly practical application of time series prediction. It is used to help enterprises identify and utilize information to reduce costs and maximize profits. For example, in numerous manufacturing enterprises, sales forecasting serves as a key indicator for inventory optimizat...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-06-01
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| Series: | Systems |
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| Online Access: | https://www.mdpi.com/2079-8954/11/6/311 |
| _version_ | 1797592537573097472 |
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| author | Daifeng Li Xin Li Fengyun Gu Ziyang Pan Dingquan Chen Andrew Madden |
| author_facet | Daifeng Li Xin Li Fengyun Gu Ziyang Pan Dingquan Chen Andrew Madden |
| author_sort | Daifeng Li |
| collection | DOAJ |
| description | Sales forecasting is a highly practical application of time series prediction. It is used to help enterprises identify and utilize information to reduce costs and maximize profits. For example, in numerous manufacturing enterprises, sales forecasting serves as a key indicator for inventory optimization and directly influences the level of cost savings. However, existing research methods mainly focus on detecting sequences and local correlations from multivariate time series (MTS), but seldom consider modeling the distinct information among the time series within MTS. The prediction accuracy of sales time series is significantly influenced by the dynamic and complex environment, so identifying the distinct signals between different time series within a sales MTS is more important. In order to extract more valuable information from sales series and to enhance the accuracy of sales prediction, we devised a universality–distinction mechanism (UDM) framework that can predict future multi-step sales. Universality represents the instinctive features of sequences and correlation patterns of sales with similar contexts. Distinction corresponds to the fluctuations in a specific time series due to complex or unobserved influencing factors. In the mechanism, a query-sparsity measurement (QSM)-based attention calculation method is proposed to improve the efficiency of the proposed model in processing large-scale sales MTS. In addition, to improve the specific decision-making scenario of inventory optimization and ensure stable accuracy in multi-step prediction, we use a joint Pin-DTW (Pinball loss and Dynamic Time Warping) loss function. Through experiments on the public Cainiao dataset, and via our cooperation with Galanz, we are able to demonstrate the effectiveness and practical value of the model. Compared with the best baseline, the improvements are 57.27%, 50.68%, and 35.26% on the Galanz dataset and 16.58%, 6.07%, and 5.27% on the Cainiao dataset, in terms of the MAE (Mean Absolute Error), MAPE (Mean Absolute Percentage Error), and RMSE (Root Mean Squared Error). |
| first_indexed | 2024-03-11T01:52:33Z |
| format | Article |
| id | doaj.art-ddf613b0a2ac4b88b34e60916fd77330 |
| institution | Directory Open Access Journal |
| issn | 2079-8954 |
| language | English |
| last_indexed | 2024-03-11T01:52:33Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Systems |
| spelling | doaj.art-ddf613b0a2ac4b88b34e60916fd773302023-11-18T12:52:48ZengMDPI AGSystems2079-89542023-06-0111631110.3390/systems11060311A Universality–Distinction Mechanism-Based Multi-Step Sales Forecasting for Sales Prediction and Inventory OptimizationDaifeng Li0Xin Li1Fengyun Gu2Ziyang Pan3Dingquan Chen4Andrew Madden5School of Information Management, Sun Yat-Sen University, Guangzhou 510275, ChinaSchool of Information Management, Sun Yat-Sen University, Guangzhou 510275, ChinaSchool of Information Management, Sun Yat-Sen University, Guangzhou 510275, ChinaSchool of Information Management, Sun Yat-Sen University, Guangzhou 510275, ChinaSchool of Information Management, Sun Yat-Sen University, Guangzhou 510275, ChinaInformation School, University of Sheffield, Sheffield S10 2TN, UKSales forecasting is a highly practical application of time series prediction. It is used to help enterprises identify and utilize information to reduce costs and maximize profits. For example, in numerous manufacturing enterprises, sales forecasting serves as a key indicator for inventory optimization and directly influences the level of cost savings. However, existing research methods mainly focus on detecting sequences and local correlations from multivariate time series (MTS), but seldom consider modeling the distinct information among the time series within MTS. The prediction accuracy of sales time series is significantly influenced by the dynamic and complex environment, so identifying the distinct signals between different time series within a sales MTS is more important. In order to extract more valuable information from sales series and to enhance the accuracy of sales prediction, we devised a universality–distinction mechanism (UDM) framework that can predict future multi-step sales. Universality represents the instinctive features of sequences and correlation patterns of sales with similar contexts. Distinction corresponds to the fluctuations in a specific time series due to complex or unobserved influencing factors. In the mechanism, a query-sparsity measurement (QSM)-based attention calculation method is proposed to improve the efficiency of the proposed model in processing large-scale sales MTS. In addition, to improve the specific decision-making scenario of inventory optimization and ensure stable accuracy in multi-step prediction, we use a joint Pin-DTW (Pinball loss and Dynamic Time Warping) loss function. Through experiments on the public Cainiao dataset, and via our cooperation with Galanz, we are able to demonstrate the effectiveness and practical value of the model. Compared with the best baseline, the improvements are 57.27%, 50.68%, and 35.26% on the Galanz dataset and 16.58%, 6.07%, and 5.27% on the Cainiao dataset, in terms of the MAE (Mean Absolute Error), MAPE (Mean Absolute Percentage Error), and RMSE (Root Mean Squared Error).https://www.mdpi.com/2079-8954/11/6/311time seriessales forecastingdeep learningmulti-step prediction |
| spellingShingle | Daifeng Li Xin Li Fengyun Gu Ziyang Pan Dingquan Chen Andrew Madden A Universality–Distinction Mechanism-Based Multi-Step Sales Forecasting for Sales Prediction and Inventory Optimization Systems time series sales forecasting deep learning multi-step prediction |
| title | A Universality–Distinction Mechanism-Based Multi-Step Sales Forecasting for Sales Prediction and Inventory Optimization |
| title_full | A Universality–Distinction Mechanism-Based Multi-Step Sales Forecasting for Sales Prediction and Inventory Optimization |
| title_fullStr | A Universality–Distinction Mechanism-Based Multi-Step Sales Forecasting for Sales Prediction and Inventory Optimization |
| title_full_unstemmed | A Universality–Distinction Mechanism-Based Multi-Step Sales Forecasting for Sales Prediction and Inventory Optimization |
| title_short | A Universality–Distinction Mechanism-Based Multi-Step Sales Forecasting for Sales Prediction and Inventory Optimization |
| title_sort | universality distinction mechanism based multi step sales forecasting for sales prediction and inventory optimization |
| topic | time series sales forecasting deep learning multi-step prediction |
| url | https://www.mdpi.com/2079-8954/11/6/311 |
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