To extend the TimeCSL system to handle online or streaming time series data, several modifications and additions can be implemented:
Incremental Learning: Incorporate techniques for incremental learning to adapt the model to new data points as they arrive. This involves updating the shapelet-based representation and the task-oriented analyzers in real-time without retraining the entire system.
Sliding Window Approach: Implement a sliding window mechanism to process incoming data in chunks or windows. This approach ensures that the system can handle continuous streams of data by updating the shapelet-based features and analysis results based on the most recent data.
Online Clustering and Anomaly Detection: Integrate online clustering algorithms like Online K-Means or Online DBSCAN to cluster streaming data points continuously. For anomaly detection, algorithms like Online Isolation Forest or Online One-Class SVM can be utilized to detect anomalies in real-time.
Efficient Memory Management: Develop strategies to manage memory efficiently, as streaming data can be vast and continuous. Techniques like data summarization, feature selection, or model compression can help reduce memory usage and improve the system's scalability.