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Unofficial Implementation of RobustPeriod: Time-Frequency Mining for Robust Multiple Periodicities Detection

robust-period 최신버젼 다운로드

최종 버전 다운로드 (.zip)

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Unofficial Implementation of RobustPeriod: Time-Frequency Mining for Robust Multiple Periodicities Detection.

> Please note that I could not fully replicate the paper, especially the optimization part. 
    In fact, I used a different optimization method rather than the said ADMM (the timing result will be greatly different from ones presented in the paper). 
    It is either the details in paper are insufficient or my understanding was lacking. I welcome any contribution.

## Installation
`pip install --upgrade git+https://github.com/ariaghora/robust-period.git`

## Usage example
```python
import numpy as np
import matplotlib.pyplot as plt
from robustperiod import robust_period, robust_period_full, plot_robust_period
from robustperiod.utils import sinewave, triangle
from statsmodels.datasets.co2.data import load_pandas

m = 1000
y1 = sinewave(m, 20, 1)
y2 = sinewave(m, 50, 1)
y3 = sinewave(m, 100, 1)
tri = triangle(m, 10)
noise = np.random.normal(0, 0.1, m)
y = y1+y2+y3+tri+noise
y[m // 2] += 10  # sudden spike

plt.plot(y)
plt.title('Dummy dataset')
plt.show()

lmb = 1e+6
c = 2
num_wavelets = 8
zeta = 1.345

periods, W, bivar, periodograms, p_vals, ACF = robust_period_full(
    y, 'db10', num_wavelets, lmb, c, zeta)
plot_robust_period(periods, W, bivar, periodograms, p_vals, ACF)
```

## Input

 <img src="resources/input.png" width=500/>

## Output

 <img src="resources/full.png" width=600/>

Please note that I hacked some parts of result presentation code so the results match the paper as close as possible.

robust-period

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