0% Complete
Home
/
13th International Conference on Computer and Knowledge Engineering
Dynamic Knowledge Enhanced Neural Fashion Trend Forecasting with Quantile Loss
Authors :
Fatemeh Rooholamini
1
Reza Azmi
2
Mobina Khademhossein
3
Maral Zarvani
4
1- Department of Computer Engineering, Faculty of Engineering, Alzahra University, Tehran, Iran
2- Department of Computer Engineering, Faculty of Engineering, Alzahra University, Tehran, Iran
3- Department of Computer Engineering, Faculty of Engineering, Alzahra University
4- Department of Computer Engineering, Faculty of Engineering, Alzahra University, Tehran, Iran
Keywords :
Fashion Trend Forecasting،Time Series Forecasting،Time Series
Abstract :
Forecasting fashion trends based on time series and style analysis is an innovative and attractive approach. Still, the dynamic nature of fashion trends and the influence of various factors make this forecast challenging. Additionally, it's important to note that previous research in this field has primarily focused on specific elements such as seasonal clothes, which may not be suitable to the evolving requirements of the fashion industry. So, we extended the Knowledge Enhanced Recurrent Network (KERN) model, which leverages deep recurrent neural networks for time series analysis to forecast fine-grained elements for a specific user group. Due to the low flexibility and the lack of management of sequences with variable memory length in the KERN method and the increasing improvement of the memory efficiency of this model, we propose a Dynamic Knowledge Enhanced Recurrent Network model (DKERN) along with the use of Quantile loss function, which is an improvement on this method. We have tried to help the fashion industry by improving forecasting fashion trends by using various analytical techniques. The proposed approaches have shown improvements ranging between 0.2% to 8.2% compared to previous works in terms of time periods, datasets, and various evaluation metrics.
Papers List
List of archived papers
Evaluating the Impact of Traveling on COVID-19 Prevalence and Predicting the New Confirmed Cases According to the Travel Rate Using Machine Learning: A Case Study in Iran
Anita Ghandehari - Soheil Shirvani - Hadi Moradi
Deep Learning-Driven Beamforming Optimization for High-Performance 5G Planar Antenna Arrays
Rahman Mohammadi - Seyed Reza Razavi Pour
A Deep CNN Model Based Ensemble Approach for Semantic and Instance Segmentation of Indoor Environment
Sajad Rezaei - Jafar Tanha - Zahra Jafari - SeyedEhsan Roshan - Mohammad-Amin Memar Kochebagh
Distilling Knowledge from CNN-Transformer Models for Enhanced Human Action Recognition
Hamid Ahmadabadi - Omid Nejati Manzari - Ahmad Ayatollahi
Reversible Data Insertion in Encryption Domain Based on Reduced Quad Difference Expansion
Alireza Ghaemi - Mohammad Zare Ehteshami - Amirhossein Ghaemi
Cross-project Defect Prediction with An Enhanced Transfer Boosting Algorithm
Nazgol Nikravesh - Mohammad Reza Keyvanpour
Early detection of Parkinson’s disease using Convolutional Neural Networks on SPECT images
Reyhaneh Dehghan - Marjan Naderan - Seyyed Enayatallah Alavi
A Self-Configurable Model for Cloud Resource Allocation
Ali Bazghandi
The application of Brain Drain Optimization algorithm on static drone placement problem
Mohammad Mehdi Samimi - Alireza Basiri
Overview of Electric Vehicles Charging Stations in Smart Grids
Mohammed Wadi - Wisam Elmasry - Mohammed Jouda - Hossein Shahinzadeh - Gevork B. Gharehpetian
more
Samin Hamayesh - Version 41.5.3