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Found 5 papers, 2 papers with code
Learning Latent Spaces for Domain Generalization in Time Series Forecasting
We propose a framework for domain generalization in time series forecasting by mining the latent factors that govern temporal dependencies across domains.
Frequency Adaptive Normalization For Non-stationary Time Series Forecasting
Time series forecasting typically needs to address non-stationary data with evolving trend and seasonal patterns.
A Survey on Societal Event Forecasting with Deep Learning
In recent years, research on event forecasting has made significant progress due to two main reasons: (1) the development of machine learning and deep learning algorithms and (2) the accessibility of public data such as social media, news sources, blogs, economic indicators, and other meta-data sources.
Causal Knowledge Guided Societal Event Forecasting
(ii) Given spatiotemporal non-independent and identically distributed (non-IID) data, modeling hidden confounders for accurate causal effect estimation is not trivial.
Graph Message Passing with Cross-location Attentions for Long-term ILI Prediction
Forecasting influenza-like illness (ILI) is of prime importance to epidemiologists and health-care providers.
