TSFpaper Project: An Overview
The TSFpaper repository is a curated collection of over 300 academic papers focused on the areas of Time Series Forecasting (TSF) and Spatio-Temporal Forecasting (STF). It serves as an essential resource for researchers, practitioners, and enthusiasts interested in the latest trends and methodologies in forecasting models.
Purpose and Scope
The primary aim of the TSFpaper project is to organize and categorize papers that present various forecasting models. The repository not only includes papers published in top conferences and journals but also features the latest research from arXiv, ensuring users have access to cutting-edge work. This dynamic repository continues to evolve, inviting contributions from the community, whether through pull requests or issue submissions to include pertinent papers.
Classification of Papers
Papers in the repository are classified primarily by the model type they address, covering three main forecasting categories:
- Univariate Time Series Forecasting:
- Focuses on predicting the future of a single variable based on its historical data.
- Multivariate Time Series Forecasting:
- Involves predicting the future of multiple variables using their historical data and involves the interaction between variables.
- Spatio-Temporal Forecasting:
- Extends traditional forecasting by incorporating spatial dimensions, crucial for applications like traffic and weather forecasting.
Each paper is tagged according to its applicable forecasting type, making it easy to navigate the repository based on specific research interests.
Key Features
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Community Contributions: Users are encouraged to contribute to this growing repository by recommending new papers or adding insights, thereby enriching the resource for everyone involved.
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Model Types and Categorization: The repository specifically marks certain standout papers and includes papers on irregular time series, long sequence modeling, and even those utilizing Large Language Models (LLM) and State Space Models (SSM).
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Additional Resources: Apart from TSFpaper, several linked repositories offer additional reading lists and toolkits for time series models, such as PyPOTS, Prophet, and Darts, providing further resources for practical application and exploration.
Recent Updates and Trends
The repository is regularly updated to include discussions and analyses of seminal works and trending methodologies. For example, recent updates highlight papers on diffusion models, state space models, and those utilizing multimodal data or Kolmogorov–Arnold Networks.
Conclusion
The TSFpaper repository represents a significant initiative in the time series forecasting domain, acting as both a comprehensive reading list and a collaborative platform for continuous learning and development. With ongoing updates and community participation, TSFpaper remains a pivotal resource in advancing time series research and applications. Whether for academic research or applied projects, this repository offers a wealth of information and tools to explore forecasting models' vast potential.