Rick

我对这个库不是很有经验,但它似乎需要一个系列而不是一个数据列表。导入pandas.pd并将数据转换为pd.Series:

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()

在不过度修改代码的情况下,我的建议是:

my_dicts =  {'key1': 'true', 'key2': 'true'}

for key in my_dicts:
    if my_dicts[key] == 'true': my_dicts[key] = 'True'

print("After:")
for key, value in my_dicts.iteritems():
    print(key + " = " + my_dicts[key])