Junxiao Li
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Junxiao Li has a total of 9 co-authors. Here are the top co-authors by number of shared publications:
- Qianting Ma
Nanjing Agricultural University, Nanjing, Jiangsu, CHINA
2 shared publications - Jinzhu Deng
College of Finance, Nanjing Agricultural University, China
1 shared publication - Junchi Shi
College of Finance, Nanjing Agricultural University, China
1 shared publication - Lanfang Yi
College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, Jiangsu, China
1 shared publication - Linping Zhang
College of Finance, Nanjing Agricultural University, China
1 shared publication - Xiaoyang Ji
College of Finance, Nanjing Agricultural University, China
1 shared publication
Latest Publications
- Open Access
- 15 November 2024
- Published by Association for Computing Machinery (ACM)
- p. 602-611
Momentum on Sports Performance: Machine Learning and Multi-Aspect Analysis
- 15 November 2024
- Published by Association for Computing Machinery (ACM)
- p. 602-611
Open AccessMomentum on Sports Performance: Machine Learning and Multi-Aspect Analysis
- Open Access
- 26 April 2024
- Published by Association for Computing Machinery (ACM)
- p. 419-425
A Novel K-means-LSTM Hybrid Model of Supply Chain Inventory Management
With the rapid development of the internet and e-commerce, demand forecasting for products and inventory optimization has become especially critical. This study develops a hybrid model that combines the K-means clustering algorithm and Long Short-Term Memory (LSTM) networks, aiming to group the three-dimensional historical data of merchants, warehouses, and product demand volumes, thereby categorizing time series and improving the accuracy of demand forecasting. This paper utilizes the shipping volume of 1996 "merchant + warehouse + product" combinations provided by JD Group from November 1 to November 11, 2022, to predict promotional trends from June 1 to June 20, 2023. Firstly, the JD warehouse products are divided into ten categories according to the K-means algorithm. Then, an LSTM model comprising an input layer, an output layer, and two hidden layers is constructed to predict the demand volume for each product category, which is subsequently verified through R² assessment. Results show that the R² maintains around 90%, indicating that the prediction model is not only reliable but also effective. The in-depth analysis and precise forecasting in this study demonstrate the practical application potential of K-means clustering and LSTM networks in enhancing forecasting accuracy, proving the significant value and robustness of the model established in the data-driven demand prediction domain.
- 26 April 2024
- Published by Association for Computing Machinery (ACM)
- p. 419-425
Open AccessA Novel K-means-LSTM Hybrid Model of Supply Chain Inventory Management
With the rapid development of the internet and e-commerce, demand forecasting for products and inventory optimization has become especially critical. This study develops a hybrid model that combines the K-means clustering algorithm and Long Short-Term Memory (LSTM) networks, aiming to group the three-dimensional historical data of merchants, warehouses, and product demand volumes, thereby categorizing time series and improving the accuracy of demand forecasting. This paper utilizes the shipping volume of 1996 "merchant + warehouse + product" combinations provided by JD Group from November 1 to November 11, 2022, to predict promotional trends from June 1 to June 20, 2023. Firstly, the JD warehouse products are divided into ten categories according to the K-means algorithm. Then, an LSTM model comprising an input layer, an output layer, and two hidden layers is constructed to predict the demand volume for each product category, which is subsequently verified through R² assessment. Results show that the R² maintains around 90%, indicating that the prediction model is not only reliable but also effective. The in-depth analysis and precise forecasting in this study demonstrate the practical application potential of K-means clustering and LSTM networks in enhancing forecasting accuracy, proving the significant value and robustness of the model established in the data-driven demand prediction domain.