1.sqrt轉換

先看序列的trend，如果有二次曲線的表現形式的話，可以做個sqrt

昨晚sqrt之後張成這樣子

原來的資料長這樣：

做個sqrt

from pandas import Series
from pandas import DataFrame
from numpy import sqrt
from matplotlib import pyplot
series = Series.from_csv('airline-passengers.csv', header=0)
dataframe = DataFrame(series.values)
dataframe.columns = ['passengers']
dataframe['passengers'] = sqrt(dataframe['passengers'])
pyplot.figure(1)
# line plot
pyplot.subplot(211)
pyplot.plot(dataframe['passengers'])
# histogram
pyplot.subplot(212)
pyplot.hist(dataframe['passengers'])
pyplot.show()
 

變成這個樣子：

還是有趨勢啊。。。。

2.log轉換

昨晚log之後也應該張這樣子

利用上面的真實資料做log

from pandas import Series
from pandas import DataFrame
from numpy import log
from matplotlib import pyplot
series = Series.from_csv('airline-passengers.csv', header=0)
dataframe = DataFrame(series.values)
dataframe.columns = ['passengers']
dataframe['passengers'] = log(dataframe['passengers'])
pyplot.figure(1)
# line plot
pyplot.subplot(211)
pyplot.plot(dataframe['passengers'])
# histogram
pyplot.subplot(212)
pyplot.hist(dataframe['passengers'])
pyplot.show()
 

表現的更加正態了，log轉換很受歡迎

3.box-cox轉換

結果圖如上

https://machinelearningmastery.com/power-transform-time-series-forecast-data-python/

• lambda = -1. is a reciprocal transform.
• lambda = -0.5 is a reciprocal square root transform.
• lambda = 0.0 is a log transform.
• lambda
  = 0.5 is a square root transform.
• lambda = 1.0 is no transform.

• from pandas import Series
  from pandas import DataFrame
  from scipy.stats import boxcox
  from matplotlib import pyplot
  series = Series.from_csv('airline-passengers.csv', header=0)
  dataframe = DataFrame(series.values)
  dataframe.columns = ['passengers']
  dataframe['passengers'] = boxcox(dataframe['passengers'], lmbda=0.0)
  pyplot.figure(1)
  # line plot
  pyplot.subplot(211)
  pyplot.plot(dataframe['passengers'])
  # histogram
  pyplot.subplot(212)
  pyplot.hist(dataframe['passengers'])
  pyplot.show()

  這裡舉了個log的例子

• 

• 神奇的是，他可以自己選一個lambda

• We can set the lambda parameter to None (the default) and let the function find a statistically tuned value.

  The following example demonstrates this usage, returning both the transformed dataset and the chosen lambda value.

• from pandas import Series
  from pandas import DataFrame
  from scipy.stats import boxcox
  from matplotlib import pyplot
  series = Series.from_csv('airline-passengers.csv', header=0)
  dataframe = DataFrame(series.values)
  dataframe.columns = ['passengers']
  dataframe['passengers'], lam = boxcox(dataframe['passengers'])
  print('Lambda: %f' % lam)
  pyplot.figure(1)
  # line plot
  pyplot.subplot(211)
  pyplot.plot(dataframe['passengers'])
  # histogram
  pyplot.subplot(212)
  pyplot.hist(dataframe['passengers'])
  pyplot.show()

  mbda: 0.148023

  1	Lambda: 0.148023