Qiqiao He

Qiqiao He
University of Macau · Department of Computer and Information Science

Doctor of Philosophy

About

6
Publications
3,993
Reads
How we measure 'reads'
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more
38
Citations
Citations since 2017
6 Research Items
38 Citations
2017201820192020202120222023051015202530
2017201820192020202120222023051015202530
2017201820192020202120222023051015202530
2017201820192020202120222023051015202530

Publications

Publications (6)
Article
Full-text available
The key challenge of Unsupervised Domain Adaptation (UDA) for analyzing time series data is to learn domain-invariant representations by capturing complex temporal dependencies. In addition, existing unsupervised domain adaptation methods for time series data are designed to align marginal distribution between source and target domains. However, ex...
Article
Full-text available
Stock movement prediction is one of the most challenging problems in time series analysis due to the stochastic nature of financial markets. In recent years, a plethora of statistical methods and machine learning algorithms were proposed for stock movement prediction. Specifically, deep learning models are increasingly applied for the prediction of...
Article
Sales volume forecasting is of great significance to E-commerce companies. Accurate sales forecasting enables managers to make reasonable resource allocation in advance. In this paper, we propose a novel approach based on Long Short-Term Memory with Particle Swam Optimization (LSTM-PSO) for sale forecasting in E-commerce companies. In the proposed...
Preprint
Full-text available
Although transfer learning is proven to be effective in computer vision and natural language processing applications, it is rarely investigated in forecasting financial time series. Majority of existing works on transfer learning are based on single-source transfer learning due to the availability of open-access large-scale datasets. However, in fi...
Chapter
Full-text available
Time-series are widely used for representing non-stationary data such as weather information, health related data, economic and stock market indexes. Many statistical methods and traditional machine learning techniques are commonly used for forecasting time series. With the development of deep learning in artificial intelligence, many researchers h...

Network

Cited By