17 citations · 37 across the 6 of their papers we have counts for
6 papers
Proactive Model Adaptation Against Concept Drift for Online Time Series Forecasting
Lifan Zhao, Yanyan Shen
Time series forecasting always faces the challenge of concept drift, where data distributions evolve over time, leading to a decline in forecast model performance. Existing solutio…
Rethinking Channel Dependence for Multivariate Time Series Forecasting: Learning from Leading Indicators
Lifan Zhao, Yanyan Shen
Recently, channel-independent methods have achieved state-of-the-art performance in multivariate time series (MTS) forecasting. Despite reducing overfitting risks, these methods mi…
Methods for Acquiring and Incorporating Knowledge into Stock Price Prediction: A Survey
Liping Wang, Jiawei Li, Lifan Zhao +6
Predicting stock prices presents a challenging research problem due to the inherent volatility and non-linear nature of the stock market. In recent years, knowledge-enhanced stock…
DoubleAdapt: A Meta-learning Approach to Incremental Learning for Stock Trend Forecasting
Lifan Zhao, Shuming Kong, Yanyan Shen
Stock trend forecasting is a fundamental task of quantitative investment where precise predictions of price trends are indispensable. As an online service, stock data continuously…
RESUS: Warm-Up Cold Users via Meta-Learning Residual User Preferences in CTR Prediction
Yanyan Shen, Lifan Zhao, Weiyu Cheng +3
Click-Through Rate (CTR) prediction on cold users is a challenging task in recommender systems. Recent researches have resorted to meta-learning to tackle the cold-user challenge,…
Mixed Information Flow for Cross-domain Sequential Recommendations
Muyang Ma, Pengjie Ren, Zhumin Chen +4
Cross-domain sequential recommendation is the task of predict the next item that the user is most likely to interact with based on past sequential behavior from multiple domains. O…