如何修复 Python 中统计模型的 Holt 和 Holt-Winters 函数中的"TypeError"

Question

我使用这样的数据

data = [253993,275396.2,315229.5,356949.6,400158.2,442431.7,495102.9,570164.8,
640993.1,704250.4,767455.4,781807.8,776332.3,794161.7,834177.7,931651.5,
1028390,1114914]

然后，我导入统计模型并使用霍尔特方法

import matplotlib.pyplot as plt
from statsmodels.tsa.holtwinters import ExponentialSmoothing, SimpleExpSmoothing, Holt
# Holt’s Method
fit1 = Holt(data).fit(smoothing_level=0.8, smoothing_slope=0.2, optimized=False)
l1, = plt.plot(list(fit1.fittedvalues) + list(fit1.forecast(5)), marker='o')
fit2 = Holt(data, exponential=True).fit(smoothing_level=0.8, smoothing_slope=0.2, optimized=False)
l2, = plt.plot(list(fit2.fittedvalues) + list(fit2.forecast(5)), marker='o')
fit3 = Holt(data, damped=True).fit(smoothing_level=0.8, smoothing_slope=0.2)
l3, = plt.plot(list(fit3.fittedvalues) + list(fit3.forecast(5)), marker='o')
l4, = plt.plot(data, marker='o')
plt.legend(handles = [l1, l2, l3, l4], labels = ["Holt's linear trend", "Exponential trend", "Additive damped trend", 'data'], loc = 'best', prop={'size': 7})
plt.show()

fit2 中抛出异常

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-17-9ce7957db4db> in <module>()
      3 l1, = plt.plot(list(fit1.fittedvalues) + list(fit1.forecast(5)), marker='o')
      4 
----> 5 fit2 = Holt(data, exponential=True)
      6 fit2.fit(smoothing_level=0.8, smoothing_slope=0.2, optimized=False)
      7 l2, = plt.plot(list(fit2.fittedvalues) + list(fit2.forecast(5)), marker='o')
g:competitionvenvlibsite-packagesstatsmodelstsaholtwinters.py in __init__(self, endog, exponential, damped)
    851     def __init__(self, endog, exponential=False, damped=False):
    852         trend = 'mul' if exponential else 'add'
--> 853         super(Holt, self).__init__(endog, trend=trend, damped=damped)
    854 
    855     def fit(self, smoothing_level=None, smoothing_slope=None, damping_slope=None, optimized=True):
g:competitionvenvlibsite-packagesstatsmodelstsaholtwinters.py in __init__(self, endog, trend, damped, seasonal, seasonal_periods, dates, freq, missing)
    389         self.trending = trend in ['mul', 'add']
    390         self.seasoning = seasonal in ['mul', 'add']
--> 391         if (self.trend == 'mul' or self.seasonal == 'mul') and (endog <= 0.0).any():
    392             raise NotImplementedError(
    393                 'Unable to correct for negative or zero values')
TypeError: '<=' not supported between instances of 'list' and 'float'

我不知道为什么，其他人都很正常。(霍尔特-温特斯的方法也是这样的(

我认为是指数参数导致了问题。那么我应该怎么做才能使用指数模型呢？

Answer 1

我对这个库不是很有经验，但它似乎想要一个系列而不是数据列表。 引入pandas.pd并将您的数据转换为pd。系列：

import matplotlib.pyplot as plt
from statsmodels.tsa.holtwinters import ExponentialSmoothing, SimpleExpSmoothing, Holt
import pandas as pd
data = [253993,275396.2,315229.5,356949.6,400158.2,442431.7,495102.9,570164.8,
640993.1,704250.4,767455.4,781807.8,776332.3,794161.7,834177.7,931651.5,
1028390,1114914]
series = pd.Series(data)
# Holt's Method
fit1 = Holt(series).fit(smoothing_level=0.8, smoothing_slope=0.2, optimized=False)
l1, = plt.plot(list(fit1.fittedvalues) + list(fit1.forecast(5)), marker='o')
fit2 = Holt(series, exponential=True).fit(smoothing_level=0.8, smoothing_slope=0.2, optimized=False)
l2, = plt.plot(list(fit2.fittedvalues) + list(fit2.forecast(5)), marker='o')
fit3 = Holt(series, damped=True).fit(smoothing_level=0.8, smoothing_slope=0.2)
l3, = plt.plot(list(fit3.fittedvalues) + list(fit3.forecast(5)), marker='o')
l4, = plt.plot(series, marker='o')
plt.legend(handles = [l1, l2, l3, l4], labels = ["Holt's linear trend", "Exponential trend", "Additive damped trend", 'data'], loc = 'best', prop={'size': 7})
plt.show()