Vortex is a scalable Scala library tailored for high-performance and fault-tolerant state space modeling over streaming data. With real-time analytics capabilities, it enables efficient processing of continuous data streams, providing insightful, actionable information.
• Real-time state space modeling for streaming data.
• High-performance, fault-tolerant processing.
• Scalable architecture for large-scale data analysis.
• Seamless integration with Scala-based streaming platforms, including a specialized ZIO module.
• /modules: The core of Vortex, organized as a multi-project SBT build.
• core: Core algorithms and functionalities for state space modeling.
• stream: Integration with streaming data sources and stream processing utilities.
• zio: ZIO-specific module for leveraging ZIO’s functional programming capabilities.
• protobuf: Utilization and configurations of Protocol Buffers for efficient serialization.
• /docs: Comprehensive documentation, including setup guides, usage examples, and API references.
• /tests: Unit and integration tests to ensure reliability and precision.
• /examples: Sample applications and scripts demonstrating Vortex in various scenarios.
- Implement state-of-the-art state space models.
- Provide tools for model fitting, diagnostics, and predictions.
- Support for various windowing operations for streaming data.
- Facilitate the handling of both regular and irregular time series.
- Implement Fast Fourier Transforms (FFT) and Wavelet Transforms for frequency domain analysis.
- Provide utilities for noise reduction, trend extraction, and signal decomposition.
- Enable real-time processing and analysis of streaming data.
- Ensure low latency and high throughput for live data feeds.
- Functional Programming in ZIO Streams:
- Leverage the power of functional programming for building robust and type-safe data pipelines.
- Integrate seamlessly with the ZIO ecosystem for asynchronous and concurrent tasks.
Achieve parity with key functionalities of the StatsModels Python project. Provide a Scala-native, type-safe, and functional approach to time series analysis. Make state space modeling and signal processing accessible and efficient in a streaming context.
Contributions to Vortex are highly appreciated! Please review our CONTRIBUTING.md for code of conduct and contribution guidelines.
Vortex is licensed under the Apache 2.0 License - detailed in the LICENSE.md file.