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源代码：import pandas as pd

import numpy as np

from statsmodels.tsa.holtwinters import ExponentialSmoothing

data ={'1962 年':[589,561,640,656,727,697,640,599,568,577,553,582],

'1963 年':[600,566,653,673,742,716,660,617,583,587,565,598],

'1964 年':[628,618,688,705,770,736,678,639,604,611,594,634],

'1965 年':[658,622,709,722,782,756,702,653,615,621,602,635],

'1966 年':[677,635,736,755,811,798,735,697,661,667,645,688],

'1967 年':[713,667,762,784,837,817,767,722,681,687,660,698],

'1968 年':[717,696,775,796,858,826,783,740,701,706,677,711],

'1969 年':[734,690,785,805,871,845,801,764,725,723,690,734],

'1970 年':[750,707,807,824,886,859,819,783,740,747,711,751],

'1971 年':[804,756,860,878,942,913,869,834,790,800,763,800],

'1972 年':[826,799,890,900,961,935,894,855,809,810,766,805],

'1973 年':[821,773,883,898,957,924,881,837,784,791,760,802],

'1974 年':[828,778,889,902,969,947,908,867,815,812,773,813],

'1975 年':[834,782,892,903,966,937,896,858,817,827,797,843]}

# 将数据转换为 DataFrame

df = pd.DataFrame(data)

# 拟合每个年份的数据

for column in df.columns:

if not df[column].isnull().any():

data_to_fit = df[column]

print(f"Fitting data for the year: {column}")

print(f"Data to fit:{data_to_fit}") # 打印查看数据

# 使用霍特-温特斯模型提取趋势效应

# 显式地设置 initialization_method 为'estimated'，即使不使用 Box-Cox 变换

model = ExponentialSmoothing(data_to_fit.values.reshape(-1, 1),

trend='add',

seasonal='add',

seasonal_periods=12,

use_boxcox=False,

initialization_method='estimated')

try:

model_fit = model.fit() # 不使用 Box-Cox 变换

trend = model_fit.fittedvalues

# 输出趋势效应

print("Fitted values:", trend)

except Exception as e:

print(f"An error occurred: {e}")

else:

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