time series forecasting
The process of predicting future values based on historical data points collected over time. This is widely used in domains such as finance, weather prediction, and operations management, often employing models that capture temporal dependencies.
- Abstain Mask Retain Core: Time Series Prediction by Adaptive Masking Loss with Representation Consistency
- DBLoss: Decomposition-based Loss Function for Time Series Forecasting
- DecompNet: Enhancing Time Series Forecasting Models with Implicit Decomposition
- Improving Time Series Forecasting via Instance-aware Post-hoc Revision
- Learning Pattern-Specific Experts for Time Series Forecasting Under Patch-level Distribution Shift
- Many Minds, One Goal: Time Series Forecasting via Sub-task Specialization and Inter-agent Cooperation
- MoFo: Empowering Long-term Time Series Forecasting with Periodic Pattern Modeling
- Not All Data are Good Labels: On the Self-supervised Labeling for Time Series Forecasting
- SEMPO: Lightweight Foundation Models for Time Series Forecasting
- Selective Learning for Deep Time Series Forecasting
- SynTSBench: Rethinking Temporal Pattern Learning in Deep Learning Models for Time Series
- TARFVAE: Efficient One-Step Generative Time Series Forecasting via TARFLOW based VAE
- TS-RAG: Retrieval-Augmented Generation based Time Series Foundation Models are Stronger Zero-Shot Forecaster
- This Time is Different: An Observability Perspective on Time Series Foundation Models
- TiRex: Zero-Shot Forecasting Across Long and Short Horizons with Enhanced In-Context Learning
- TimeEmb: A Lightweight Static-Dynamic Disentanglement Framework for Time Series Forecasting
- Toward Relative Positional Encoding in Spiking Transformers
- xLSTM-Mixer: Multivariate Time Series Forecasting by Mixing via Scalar Memories