ISKE2023 Special Session on Deep Learning for Time Series Forecasting
The 2023 IEEE International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2023) is the 18th in a series of ISKE conferences. The conference will be held in Fuzhou, China during November 17-19, 2023. The conference proceedings will be published by IEEE Press (EI indexed). Special issues of SCI indexed journals will be devoted to a strict refereed selection of extended papers presented at ISKE 2023.
Scope and Motivation
Time series forecasting problems have been widely seen in different fields, e.g., traffic flow and speed forecasting in the transportation domain, air quality forecasting in the environmental domain, load forecasting in the energy domain, stock market forecasting in the financial domain, and wireless traffic forecasting in the telecommunication domain. Traditional time series forecasting models include AutoRegressive Integrated Moving Average (ARIMA) and Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) have been proven less effective than data-driven methods, e.g., machine learning models, when the time forecasting problems become more complex today. In more recent years, to model the nonlinear and complex dependency in the time series, deep learning models have been introduced to solve time series forecasting and to capture both spatial and temporal dependencies. Deep learning models have achieved state-of-the-art performance in a series of time series forecasting problems. In this special session, we aim to collect the studies that explore the application of deep learning for time series forecasting problems.
The topics of interest include, but are not limited to:
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New models of deep learning for time series forecasting problems, e.g., convolution neural networks, recurrent neural networks, Transformers, graph convolutional and graph attention networks, spatio-temporal graph neural networks, etc.
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Applications of deep learning for time series forecasting problems, e.g., traffic flow and speed forecasting in the transportation domain, air quality forecasting in the environmental domain, load forecasting in the energy domain, stock market forecasting in the financial domain, wireless traffic forecasting in the telecommunication domain, etc.
- Open data and source resources for time series forecasting problems
Important Dates
June 30, 2023
July 31, 2023
Full paper submission
July 31, 2023
August 31, 2023
Acceptance notification
Sept. 30, 2023
October 30, 2023
Camera-ready paper submission
Paper Submission
The authors are required to submit their papers to a Special Session following the steps below:
Contact Us
- Weiwei Jiang
- Email: jww@bupt.edu.cn
- Telephone: (+86) 15210560396
- Address:School of Information and Communication Engineering
- Beijing University of Posts and Telecommunications, Beijing, China