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What is Time Series Segmentation for Prediction about?

This document presents two proposals for time series segmentation based on stationarity analysis to enhance prediction accuracy for new samples, particularly in IoT applications. The first method, Change Detector segmentation, utilizes change detection algorithms, while the second, ADF-based segmentation, employs the Augmented Dickey-Fuller test for identifying non-stationary segments. Experimental results demonstrate significant improvements in prediction accuracy, particularly with deep learning models, h

Author

tfaresisam

Language

EN