UniTS: A unified multi-task time series model
Although pre-trained transformers and reprogrammed text-based LLMs have shown strong
performance on time series tasks, the best-performing architectures vary widely across …
performance on time series tasks, the best-performing architectures vary widely across …
Cyclenet: enhancing time series forecasting through modeling periodic patterns
S Lin, W Lin, X Hu, W Wu, R Mo… - Advances in Neural …, 2025 - proceedings.neurips.cc
The stable periodic patterns present in time series data serve as the foundation for
conducting long-horizon forecasts. In this paper, we pioneer the exploration of explicitly …
conducting long-horizon forecasts. In this paper, we pioneer the exploration of explicitly …
MGSFformer: A multi-granularity spatiotemporal fusion transformer for air quality prediction
Air quality spatiotemporal prediction can provide technical support for environmental
governance and sustainable city development. As a classic multi-source spatiotemporal …
governance and sustainable city development. As a classic multi-source spatiotemporal …
Time-moe: Billion-scale time series foundation models with mixture of experts
Deep learning for time series forecasting has seen significant advancements over the past
decades. However, despite the success of large-scale pre-training in language and vision …
decades. However, despite the success of large-scale pre-training in language and vision …
PGN: The RNN's New Successor is Effective for Long-Range Time Series Forecasting
Due to the recurrent structure of RNN, the long information propagation path poses
limitations in capturing long-term dependencies, gradient explosion/vanishing issues, and …
limitations in capturing long-term dependencies, gradient explosion/vanishing issues, and …
Channel-aware low-rank adaptation in time series forecasting
The balance between model capacity and generalization has been a key focus of recent
discussions in long-term time series forecasting. Two representative channel strategies are …
discussions in long-term time series forecasting. Two representative channel strategies are …
Not all frequencies are created equal: towards a dynamic fusion of frequencies in time-series forecasting
Long-term time series forecasting is a long-standing challenge in various applications. A
central issue in time series forecasting is that methods should expressively capture long …
central issue in time series forecasting is that methods should expressively capture long …
A Comprehensive Survey of Time Series Forecasting: Architectural Diversity and Open Challenges
Time series forecasting is a critical task that provides key information for decision-making
across various fields. Recently, various fundamental deep learning architectures such as …
across various fields. Recently, various fundamental deep learning architectures such as …
How Sophisticated Are Neural Networks Needed to Predict Long-Term Nonadiabatic Dynamics?
Nonadiabatic dynamics is key for understanding solar energy conversion and
photochemical processes in condensed phases. This often involves the non-Markovian …
photochemical processes in condensed phases. This often involves the non-Markovian …
[HTML][HTML] Deep Learning Models for PV Power Forecasting
J Yu, X Li, L Yang, L Li, Z Huang, K Shen, X Yang… - Energies, 2024 - mdpi.com
Accurate forecasting of photovoltaic (PV) power is essential for grid scheduling and energy
management. In recent years, deep learning technology has made significant progress in …
management. In recent years, deep learning technology has made significant progress in …