# coding:utf-8
# 用 ARMA 进行时间序列预测
import pandas as pd
import matplotlib.pyplot as plt
import statsmodels.api as sm
from statsmodels.tsa.arima_model import ARMA
from statsmodels.graphics.api import qqplot
# 创建数据
data = [5922, 5308, 5546, 5975, 2704, 1767, 4111, 5542, 4726, 5866, 6183, 3199, 1471, 1325, 6618, 6644, 5337, 7064, 2912, 1456, 4705, 4579, 4990, 4331, 4481, 1813, 1258, 4383, 5451, 5169, 5362, 6259, 3743, 2268, 5397, 5821, 6115, 6631, 6474, 4134, 2728, 5753, 7130, 7860, 6991, 7499, 5301, 2808, 6755, 6658, 7644, 6472, 8680, 6366, 5252, 8223, 8181, 10548, 11823, 14640, 9873, 6613, 14415, 13204, 14982, 9690, 10693, 8276, 4519, 7865, 8137, 10022, 7646, 8749, 5246, 4736, 9705, 7501, 9587, 10078, 9732, 6986, 4385, 8451, 9815, 10894, 10287, 9666, 6072, 5418]
data=pd.Series(data)
data_index = sm.tsa.datetools.dates_from_range('1901','1990')
# 绘制数据图
data.index = pd.Index(data_index)
data.plot(figsize=(12,8))
plt.show()
# 创建 ARMA 模型 # 创建 ARMA 模型
arma = ARMA(data,(7,0)).fit()
print('AIC: %0.4lf' %arma.aic)
# 模型预测
predict_y = arma.predict('1990', '2000')
# 预测结果绘制
fig, ax = plt.subplots(figsize=(12, 8))
ax = data.ix['1901':].plot(ax=ax)
predict_y.plot(ax=ax)
plt.show()

# coding:utf-8
# 用 ARMA 进行时间序列预测
import pandas as pd
import matplotlib.pyplot as plt
import statsmodels.api as sm
from statsmodels.tsa.arima_model import ARMA
from statsmodels.graphics.api import qqplot
# 创建数据
data = [5922, 5308, 5546, 5975, 2704, 1767, 4111, 5542, 4726, 5866, 6183, 3199, 1471, 1325, 6618, 6644, 5337, 7064, 2912, 1456, 4705, 4579, 4990, 4331, 4481, 1813, 1258, 4383, 5451, 5169, 5362, 6259, 3743, 2268, 5397, 5821, 6115, 6631, 6474, 4134, 2728, 5753, 7130, 7860, 6991, 7499, 5301, 2808, 6755, 6658, 7644, 6472, 8680, 6366, 5252, 8223, 8181, 10548, 11823, 14640, 9873, 6613, 14415, 13204, 14982, 9690, 10693, 8276, 4519, 7865, 8137, 10022, 7646, 8749, 5246, 4736, 9705, 7501, 9587, 10078, 9732, 6986, 4385, 8451, 9815, 10894, 10287, 9666, 6072, 5418]
data=pd.Series(data)
data_index = sm.tsa.datetools.dates_from_range('1901','1990')
# 绘制数据图
data.index = pd.Index(data_index)
data.plot(figsize=(12,8))
plt.show()
# 创建 ARMA 模型 # 创建 ARMA 模型
arma = ARMA(data,(7,0)).fit()
print('AIC: %0.4lf' %arma.aic)
# 模型预测
predict_y = arma.predict('1990', '2000')
# 预测结果绘制
fig, ax = plt.subplots(figsize=(12, 8))
ax = data.ix['1901':].plot(ax=ax)
predict_y.plot(ax=ax)
plt.show()

# coding:utf-8 # 用 ARMA 进行时间序列预测 # 创建数据 # 绘制数据图 # 创建 ARMA 模型 # 创建 ARMA 模型 # 模型预测 # 预测结果绘制