Complexity Testing for Time Series with Fast Approximate Entropy Algorithm
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Resource Overview
Implementation of fast approximate entropy algorithm for time series complexity analysis, capable of processing both deterministic and random signals with significantly improved computational efficiency
Detailed Documentation
For complexity testing of time series data, we implement a fast approximate entropy algorithm that can effectively analyze and characterize signal properties, whether dealing with deterministic signals or random signals. This algorithm operates by comparing patterns of varying lengths within the time series and calculating the probability of pattern recurrence. The implementation typically involves vectorizing the pattern matching process and optimizing distance calculations between data segments. By employing this approach, we achieve substantial acceleration in analysis speed while maintaining accuracy, enabling better understanding of time series characteristics through quantitative complexity measures. The key functions include pattern template generation, distance threshold application, and probability ratio computation for different pattern lengths.
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