#Time Series Forecasting

NeuralProphet integrates neural networks with traditional algorithms on PyTorch, providing an accessible platform for high-frequency time series prediction. Designed for collaborative development, it supports rapid model customization, emphasizing clarity and adaptability. While initial predictions may require tuning, iterative refinement results in precise models. Optimized for datasets covering a minimum of two years, it includes features like autoregression and seasonality. Participate in community discussions on GitHub and Slack, and utilize comprehensive tutorials. A suitable choice for developers exploring open-source forecasting solutions.

The LTSF-Linear project introduces a set of linear models that enhance time series forecasting capabilities, surpassing the performance of traditional Transformers. These include the Linear, NLinear, and DLinear models, crafted to effectively accommodate trend, seasonality, and distribution variations. Offering high efficiency with low memory and parameter requirements, these models also provide interpretability via weight visualization. The project includes well-documented Python implementations and benchmarks, supporting both univariate and multivariate forecasting tasks. The models efficiently streamline the training process and enable quick inference, showing notable improvements over existing methods in capturing temporal dynamics.

Autoformer is an innovative forecasting model enhancing traditional Transformers with deep decomposition and auto-correlation mechanisms, achieving a 38% enhancement in long-term forecasting for areas like energy and weather. It offers practical applications with speed-optimized architecture and integrates with platforms such as Hugging Face.

The repository provides a comprehensive collection of over 300 papers on time series and spatio-temporal forecasting, categorized by model type. It is regularly updated with the latest studies from leading conferences, journals, and arXiv, supporting various kinds of forecasting such as univariate, multivariate, and spatio-temporal. It explains complex concepts and how deep learning affects model flexibility, and explores emerging subjects like irregular time series and recent innovations like the Mamba model. Contributions of relevant papers are welcome to further enrich this forecasting research resource.