#Forecasting

TSLib is an open-source library tailored for deep time series analysis, enabling effective forecasting, imputation, anomaly detection, and classification. It features models such as TimeXer, which leverages exogenous variables, and the OpenLTM pre-trained paradigm. Regular updates with models like iTransformer are supported, alongside well-documented benchmarks and tutorials, making TSLib a valuable resource for researchers in the field.

Discover a wide range of resources on Transformers for time series, which outlines recent innovations in the area. This well-curated repository grants access to influential papers, codes, and datasets aimed at advancing the comprehension and modeling of time series using Transformers. Regularly updated, the repository addresses various uses such as forecasting, anomaly detection, and classification. Contributions of new resources or error corrections are welcomed. This repository serves as an important resource for AI specialists interested in time series analysis, delivering insights from leading AI conferences and journals. It aids ongoing AI research and development for time series, supplying essential tools and references for both novices and experienced researchers.

Discover neural forecasting models in the 'NeuralForecast' library, which support over 30 models including RNNs, LSTMs, and Transformers like Informer and AutoFormer. Designed for usability and robustness, it offers features like probabilistic forecasting, exogenous variable support, and automatic hyperparameter tuning. Its sklearn-like syntax allows easy implementation of .fit and .predict functions, suitable for improving forecasting accuracy or exploring new models.

Merlion is a machine learning library designed for time series analysis, focusing on forecasting, anomaly detection, and change point detection. It supports both univariate and multivariate datasets with features like standardized data loading and diverse model options, including statistical, ensemble, and deep learning methods, as well as AutoML for effective hyperparameter tuning. The library offers a straightforward interface with visualizations and scalable distributed computation for industrial applications. Its evaluation pipeline mimics real-world conditions, supporting benchmarking and enhanced model performance. Simple installation and detailed documentation make it suitable for engineers and researchers developing custom models.

The repository provides detailed code and insights for improving data analysis using time series and machine learning. It explores methods such as Proxy SHAP, gradient boosting, and hybrid modeling, accompanied by Python code available on the linked GitHub repository. Focused on real-time prediction, anomaly detection, and forecast accuracy, it supports users in enhancing their analytical skills. This resource includes practical guidance for machine learning applications in predictive maintenance, data drift detection, and more.

TimesFM is a powerful time-series forecasting model developed by Google Research. It features seamless integration with Hugging Face for efficient model inference. Major updates include comprehensive PyTorch support, finetuning options, and innovative zero-shot covariate integration. TimesFM is specially designed for univariate time-series predictions and supports both long and short horizon forecasting. Its versatility is further enhanced by easy installation through PyPI, optimized for PAX and PyTorch platforms, which makes it suitable for diverse computational settings.