#Time Series

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.

Explore a Python package for Bayesian time series forecasting and inference, featuring models such as Exponential Smoothing and Local Global Trend with advanced sampling methods like Markov-Chain Monte Carlo. Access extensive documentation and community support for efficient implementation, suitable for developers and data scientists looking for intuitive probabilistic programming tools.

NxSignal provides versatile tools for digital signal processing with the Nx library in Elixir. It facilitates time series analysis through Fourier Transforms and filters such as FIR and IIR, catering to audio processing and broader signal applications. Integrate NxSignal easily into your Elixir Mix project to utilize various backends, leveraging its Nx.Defn foundation. Detailed guides in the repository expand opportunities for learning and community contributions.

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.

OATS delivers a reliable time series anomaly detection system utilizing advanced methods. It supports univariate and multivariate data, providing consistent outputs across models. Its modular structure facilitates integration into diverse projects. Key features include user-friendly model interfaces, options for setting prediction thresholds, and compatibility with deep learning frameworks such as PyTorch and TensorFlow. The project invites open-source contributions, with comprehensive documentation available to support setup and implementation for enhanced detection adaptability.

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.

The Anomaly Transformer project presents a novel method for detecting anomalies in time series data through association discrepancy. It incorporates an Anomaly-Attention mechanism and a minimax strategy to distinguish between normal and abnormal data effectively. Pre-processed datasets and experiment scripts are included for reproducibility of results.

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.