Welcome to Time Series RAG’s documentation!

Contents:

• Overview
  • Time Series RAG
• Models
  • Time Series Embedding
• RAG System
  • Time Series RAG Implementation
• Web API
  • FastAPI Web Application
• Analytics
  • Time Series Analytics
• Quick Start Guide
  • Basic Example
  • Visualization Example
  • Running the Examples
• Pattern Recognition
  • Pattern Extraction
  • Pattern Search
  • Seasonality Analysis
  • Running the Examples
• Anomaly Detection
  • Synthetic Data Generation
  • Anomaly Detection
  • Anomaly Pattern Analysis
  • Running the Examples

Time Series RAG is a powerful system for time series similarity search and retrieval augmented generation.

Features

• Time series embedding using statistical features and resampling

• Efficient similarity search with FAISS

• Interactive web interface with Plotly visualizations

• RESTful API for integration

• Support for metadata and context retrieval

Quick Start

1. Install the package:

   pip install -r requirements.txt
   

2. Run the web application:

   python app.py
   

3. Access the web interface at http://localhost:50758

Indices and tables

• Index

• Module Index

• Search Page