Traditional inference techniques and infrastructure tools can be illsuited to time-series data, which may be noisy, streaming, multispectral, irregularly sampled, and/or extremely large. This BoF is aimed at identifying promising approaches to time series analysis across a diverse set of use cases and finding projects of common interest for potential future collaborations.
- Share use cases, tooling & pain points around time series analysis and inference
- Identify common tools & difficulties across use cases
- Open issues discussions
- Find potential cross-domain/cross-methodology areas for future collaboration
- How to deal with "too much data" -- sensors generating more data than can bear sent to analysis pipeline (e.g., radio astronomy, high-energy physics)
- Rapid/real-time inference with limited data (seismology)
- Dealing with concept drift; online/incremental models
- Databases/query mechanisms for time-series (e.g., InfluxDB)
- Inference influencing outcomes influencing inference... (e.g., reinforcement learning)
- Anomaly detection
- Handling noisy, uncertain, irregularly sampled data
"sensors generating more data than can bear sent to analysis pipeline" -- maybe "that the analysis pipeline can bear" or "than can be consumed by the analysis pipeline" ?