Beyond classification, clustering, and anomaly detection, the TimeCSL framework can be extended to incorporate various other time series analysis tasks, such as:
Forecasting: Integrate time series forecasting models like ARIMA, LSTM, or Prophet to predict future values based on historical data. The shapelet-based representation can provide valuable insights for forecasting tasks by capturing relevant patterns in the data.
Dimensionality Reduction: Implement techniques like Principal Component Analysis (PCA) or t-SNE to reduce the dimensionality of time series data while preserving important information. This can aid in visualization and exploratory data analysis within the TimeCSL framework.
Change Point Detection: Incorporate algorithms to detect abrupt changes or transitions in time series data. By analyzing the shapelet-based features, the system can identify significant deviations in the data distribution, signaling potential change points.
Time Series Segmentation: Develop methods to segment time series data into meaningful subsequences based on shapelet similarities. This can assist in identifying distinct patterns or events within the data, enabling more granular analysis and interpretation.