streamingsmodeller
Streamingsmodeller are computational models designed to process and analyze data that arrive in a continuous sequence, or data streams, with the aim of producing timely predictions or insights. They differ from traditional batch models, which work on fixed datasets processed offline.
Key requirements for streamingsmodeller include one-pass data processing, online learning capabilities, memory efficiency, and the ability
Common approaches in streamingsmodeller include online regression and classification methods such as stochastic gradient descent, online
Applications span real-time fraud detection, network monitoring, dynamic recommender updates, telemetry from IoT sensors, and anomaly
Evaluation typically uses prequential (predictive sequential) metrics, measures of latency and throughput, and assessments of memory
The term streamingsmodeller is used in Scandinavian contexts to describe models designed for streaming data and