domain
general
task
classification
imputation
prediction/forecasting
anomaly detection
Journal/Conference
ICLR2023
challenge
1. Complex Temporal Patterns: The intermixing and overlapping of various changes (such as rising, falling, fluctuating, etc.) in time series add complexity to modeling efforts.
2. Long-term Dependencies: Existing methods like RNNs, TCNs, and Transformers struggle to effectively capture long-term dependencies in time series.
3. Multi-periodicity: Time series often exhibit multi-periodicity, where the interaction of multiple periods further complicates the modeling process.
method
CNN/TCN