Piecewise Aggregate Approximation

2024-07-09 14:27| 来源: 网络整理| 查看: 265

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Piecewise Aggregate Approximation露

Time series with a high sampling rate can be very noisy. In order to reduce noise, a technique called Piecewise Aggregate Approximation was invented, consisting in taking the mean over back-to-back points. This decreases the number of points and reduces noise while preserving the trend of the time series. This example illustrates the transformation. It is implemented as pyts.approximation.PiecewiseAggregateApproximation.

# Author: Johann Faouzi # License: BSD-3-Clause import numpy as np import matplotlib.pyplot as plt from pyts.approximation import PiecewiseAggregateApproximation # Parameters n_samples, n_timestamps = 100, 48 # Toy dataset rng = np.random.RandomState(41) X = rng.randn(n_samples, n_timestamps) # PAA transformation window_size = 6 paa = PiecewiseAggregateApproximation(window_size=window_size) X_paa = paa.transform(X) # Show the results for the first time series plt.figure(figsize=(6, 4)) plt.plot(X[0], 'o--', ms=4, label='Original') plt.plot(np.arange(window_size // 2, n_timestamps + window_size // 2, window_size), X_paa[0], 'o--', ms=4, label='PAA') plt.vlines(np.arange(0, n_timestamps, window_size) - 0.5, X[0].min(), X[0].max(), color='g', linestyles='--', linewidth=0.5) plt.legend(loc='best', fontsize=10) plt.xlabel('Time', fontsize=12) plt.title('Piecewise Aggregate Approximation', fontsize=16) plt.show()

Total running time of the script: ( 0 minutes 0.430 seconds)

Download Python source code: plot_paa.py Download Jupyter notebook: plot_paa.ipynb

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