Market product demand forecasting method based on probability statistics and convolution neural network

2021 ◽  
Vol 25 (2) ◽  
pp. 187
Author(s):  
Yingji Cui
Keyword(s):  
Neural Network ◽  
Demand Forecasting ◽  
Convolution Neural Network ◽  
Product Demand ◽  
Forecasting Method ◽  
Market Product

Product Demand Forecasting in Ecommerce Based on Nonlinear Autoregressive Neural Network

2021 ◽  
Author(s):  
Peijian Wu ◽  
Yulu Chen
Keyword(s):  
Neural Network ◽  
Demand Management ◽  
Time Series Prediction ◽  
Demand Forecasting ◽  
Set Partition ◽  
Data Set ◽  
Product Demand ◽  
Real Scene ◽  
Nonlinear Autoregressive ◽  
Autoregressive Neural Network

Abstract With the rapid growth of the e-commerce business scale, to meet customers' demand for efficient order processing, it is of great significance to establish an order management mechanism capable of responding quickly by accurately predicting product demand. This study used real e-commerce order demand data and established a nonlinear autoregressive neural network (NAR) model after pre-processing methods including down-sampling and data set partition to effectively forecast the demand of products in the next 13 weeks. Compared with the Prophet time series prediction framework, NAR had better generalization ability, and the prediction time was reduced by 18.54%. Finally, we summarized two methods' characteristics and gave instructions on applying our model in the real scene. After being deployed in the actual demand management, the trained artificial neural network provides a scientific reference for the data-driven e-commerce decision-making process and brings new advantages over other companies, achieving the rational allocation of resources.

scholarly journals Demand Forecasting for Multichannel Fashion Retailers by Integrating Clustering and Machine Learning Algorithms

Processes ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 1578
Author(s):  
I-Fei Chen ◽  
Chi-Jie Lu
Keyword(s):  
Demand Uncertainty ◽  
Prediction Models ◽  
Demand Forecasting ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Fashion Industry ◽  
Fast Fashion ◽  
Product Demand ◽  
Learning Machines ◽  
Forecasting Method

In today’s rapidly changing and highly competitive industrial environment, a new and emerging business model—fast fashion—has started a revolution in the apparel industry. Due to the lack of historical data, constantly changing fashion trends, and product demand uncertainty, accurate demand forecasting is an important and challenging task in the fashion industry. This study integrates k-means clustering (KM), extreme learning machines (ELMs), and support vector regression (SVR) to construct cluster-based KM-ELM and KM-SVR models for demand forecasting in the fashion industry using empirical demand data of physical and virtual channels of a case company to examine the applicability of proposed forecasting models. The research results showed that both the KM-ELM and KM-SVR models are superior to the simple ELM and SVR models. They have higher prediction accuracy, indicating that the integration of clustering analysis can help improve predictions. In addition, the KM-ELM model produces satisfactory results when performing demand forecasting on retailers both with and without physical stores. Compared with other prediction models, it can be the most suitable demand forecasting method for the fashion industry.

Identification of Violence Images Using Convolution Neural Network in Social Network

2020 ◽  
Vol 5 (3) ◽  
pp. 34-38
Author(s):  
Santhi Baskaran ◽  
Jahnavi Korrapati ◽  
Sooriya K. ◽  
Pavithra R.
Keyword(s):  
Neural Network ◽  
Social Network ◽  
Convolution Neural Network

The Convolution Neural Network Combined with the HT Person Fit Statistic to Develop an APP for Detecting Dengue Fever in Children: Development and Usability Study (Preprint)

2019 ◽  
Author(s):  
CHIEN WEI ◽  
Chi Chow Julie ◽  
Chou Willy
Keyword(s):  
Neural Network ◽  
Dengue Fever ◽  
Prediction Accuracy ◽  
Early Stage ◽  
Family Members ◽  
Convolution Neural Network ◽  
Public Health Issue ◽  
Person Fit ◽  
Model Accuracy ◽  
Comparison Results

UNSTRUCTURED Backgrounds: Dengue fever (DF) is an important public health issue in Asia. However, the disease is extremely hard to detect using traditional dichotomous (i.e., absent vs. present) evaluations of symptoms. Convolution neural network (CNN), a well-established deep learning method, can improve prediction accuracy on account of its usage of a large number of parameters for modeling. Whether the HT person fit statistic can be combined with CNN to increase the prediction accuracy of the model and develop an application (APP) to detect DF in children remains unknown. Objectives: The aim of this study is to build a model for the automatic detection and classification of DF with symptoms to help patients, family members, and clinicians identify the disease at an early stage. Methods: We extracted 19 feature variables of DF-related symptoms from 177 pediatric patients (69 diagnosed with DF) using CNN to predict DF risk. The accuracy of two sets of characteristics (19 symptoms and four other variables, including person mean, standard deviation, and two HT-related statistics matched to DF+ and DF−) for predicting DF, were then compared. Data were separated into training and testing sets, and the former was used to predict the latter. We calculated the sensitivity (Sens), specificity (Spec), and area under the receiver operating characteristic curve (AUC) across studies for comparison. Results: We observed that (1) the 23-item model yields a higher accuracy rate (0.95) and AUC (0.94) than the 19-item model (accuracy = 0.92, AUC = 0.90) based on the 177-case training set; (2) the Sens values are almost higher than the corresponding Spec values (90% in 10 scenarios) for predicting DF; (3) the Sens and Spec values of the 23-item model are consistently higher than those of the 19-item model. An APP was subsequently designed to detect DF in children. Conclusion: The 23-item model yielded higher accuracy rates (0.95) and AUC (0.94) than the 19-item model (accuracy = 0.92, AUC = 0.90). An APP could be developed to help patients, family members, and clinicians discriminate DF from other febrile illnesses at an early stage.

Sign in / Sign up

Export Citation Format

Share Document