评估指标

\text{MAE} = \frac{1}{n} \sum_{i=1}^{n}|\hat{y}_i-y_i|

\text{MSE} = \frac{1}{n}\sum_{i=1}^{n}(\hat{y}_i - y_i)^{2}

\text{MAPE} = \frac{1}{n} \sum_{i=1}^{n} \left|\frac{\hat{y}_i-y_i}{y_i}\right|

\text{R}^2 = 1- \frac{ \sum_{i=1}^{n}(\hat{y}i - y_i)^{2}}{ \sum{i=1}^{n}(\overline{y} - y_i)^{2}}

1import pandas as pd
2from statsmodels.tsa.holtwinters import ExponentialSmoothing
3from sklearn.metrics import *
4import warnings
5warnings.filterwarnings("ignore")
6
7df = pd.read_csv('airpassengers.csv')
8
9# 转换数据类型
10df["Month"] = pd.to_datetime(df['Month'], format='%Y-%m')
11 
12# 将日期设为索引
13df = df.set_index('Month')
14
15# 指数平滑法（Holt-Winters）
16passengers = df['Passengers']
17results = ExponentialSmoothing(
18    passengers,
19    trend='multiplicative',
20    seasonal='multiplicative',
21    freq='MS').fit()
22
23predictions = results.predict(start=df.index.min(), end=df.index.max())
24
25# 评估指标
26mae = mean_absolute_error(passengers, predictions)
27mse = mean_squared_error(passengers, predictions)
28mape = mean_absolute_percentage_error(passengers, predictions)
29r2 = r2_score(passengers, predictions)
30
31print("MAE: {0}".format(mae))
32print("MSE: {0}".format(mse))
33print("MAPE: {0}".format(mape))
34print("R2: {0}".format(r2))