目录
参考https://www.zhihu.com/question/21229371
参考https://www.analyticsvidhya.com/blog/2018/02/time-series-forecasting-methods/
比较流行的还有holt winters、arima等，在statsmodels这个lib里就有：
参考https://www.jianshu.com/p/2c607fe926f0
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from statsmodels.tsa.holtwinters import SimpleExpSmoothing
x1 = np.linspace(0, 1, 100)
y1 = pd.Series(np.multiply(x1, (x1 - 0.5)) + np.random.randn(100))
ets1 = SimpleExpSmoothing(y1)
r1 = ets1.fit()
pred1 = r1.predict(start=len(y1), end=len(y1) + len(y1)//2)
pd.DataFrame({
'origin': y1,
'fitted': r1.fittedvalues,
'pred': pred1
}).plot(legend=True)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from statsmodels.tsa.holtwinters import Holt
x2 = np.linspace(0, 99, 100)
y2 = pd.Series(0.1 * x2 + 2 * np.random.randn(100))
ets2 = Holt(y2)
r2 = ets2.fit()
pred2 = r2.predict(start=len(y2), end=len(y2) + len(y2)//2)
pd.DataFrame({
'origin': y2,
'fitted': r2.fittedvalues,
'pred': pred2
}).plot(legend=True)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from statsmodels.tsa.holtwinters import ExponentialSmoothing
x3 = np.linspace(0, 4 * np.pi, 100)
y3 = pd.Series(20 + 0.1 * np.multiply(x3, x3) + 8 * np.cos(2 * x3) + 2 * np.random.randn(100))
ets3 = ExponentialSmoothing(y3, trend='add', seasonal='add', seasonal_periods=25)
r3 = ets3.fit()
pred3 = r3.predict(start=len(y3), end=len(y3) + len(y3)//2)
pd.DataFrame({
'origin': y3,
'fitted': r3.fittedvalues,
'pred': pred3
}).plot(legend=True)
https://www.tensorflow.org/api_docs/python/tf/contrib/timeseries
https://machinelearningmastery.com/time-series-forecasting-long-short-term-memory-network-python/