我用Python来拟合一个正弦函数的时间序列。我找到了一个很好的匹配,现在我想能够预测未来的价值。。我在这里迷路了。在
我得到的是:
timeSeries = [0.01146, 0.00724, 0.00460, 0.00192, 0.00145, 0.01559, 0.02585, 0.04118, 0.05073, 0.01966, 0.01486, 0.02784]
import numpy as np
from scipy.optimize import curve_fit
def createSinFromFit(x, freq, amplitude, phase, offset):
return np.sin(x * freq + phase) * amplitude + offset
def sinRegr(series):
t = np.linspace(0, 4*np.pi, len(series))
guess_freq = 1
guess_amplitude = 3*np.std(series)/(2**0.5)
guess_phase = 0
guess_offset = np.mean(series)
p0=[guess_freq, guess_amplitude, guess_phase, guess_offset]
fit = curve_fit(createSinFromFit, t, series, p0=p0)
results = createSinFromFit(t,*fit[0])
return results
plotThis = sinRegr(timeSeries)
此代码生成您在图中看到的配件:
如何扩展sin函数,使之预测序列的未来点?i、 e.怎样才能让正弦图的跨度在右边,超出“已知”数据点覆盖的区域?在
您需要区分数据时间线(输入)和拟合时间线(输出)。一旦你这样做,方法就相当清楚了。下面我把它们叫做
tdata
和tfit
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