A novel unsupervised anomaly detection method for time series using Haar wavelets and a designed t-test outperforms state-of-the-art benchmarks across 343 datasets.
Y., Harraz, N., & Eltawil, A
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
UNVERDICTED 2representative citing papers
Small feedforward neural networks embedded in MILP surgical scheduling models deliver the fastest solutions with optimality gaps below 2%, highest utilization in most cases, and simulated overtime closest to targets on hospital data.
citing papers explorer
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Fast and Accurate Anomaly Detection in Time Series
A novel unsupervised anomaly detection method for time series using Haar wavelets and a designed t-test outperforms state-of-the-art benchmarks across 343 datasets.
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Handling Overtime Constraints in Mixed Integer Linear Programming for Surgical Scheduling: A Comparison of Neural Network and Classical Linearization Techniques
Small feedforward neural networks embedded in MILP surgical scheduling models deliver the fastest solutions with optimality gaps below 2%, highest utilization in most cases, and simulated overtime closest to targets on hospital data.