Prophet

1import pandas as pd
2import matplotlib.pyplot as plt
3from prophet import Prophet
4import logging
5
6# 禁用日志输出
7logger = logging.getLogger('cmdstanpy')
8logger.addHandler(logging.NullHandler())
9logger.propagate = False
10logger.setLevel(logging.CRITICAL)
11
12df = pd.read_csv('airpassengers.csv')
13
14# 转换数据类型
15df['Month'] = pd.to_datetime(df['Month'], format='%Y-%m')
16 
17# 重命名列
18df = df.rename(columns={'Month': 'ds', 'Passengers': 'y'})
19
20# 使用 Prophet 模型
21model = Prophet()
22model.add_country_holidays(country_name='US')
23model.fit(df)
24
25future = model.make_future_dataframe(periods=24)
26predictions = model.predict(future)
27
28# 绘图
29fig, ax = plt.subplots(figsize=(7, 4.5), dpi=300)
30fig = model.plot(predictions, xlabel='日期', ylabel='乘客数量')
31
32fig.subplots_adjust(top=0.95)
33plt.title("航空公司乘客数量预测")
34plt.legend()
35
36fig.savefig("output/output.png")
37plt.close(fig)

1import pandas as pd
2import matplotlib.pyplot as plt
3from prophet import Prophet
4import logging
5
6# 禁用日志输出
7logger = logging.getLogger('cmdstanpy')
8logger.addHandler(logging.NullHandler())
9logger.propagate = False
10logger.setLevel(logging.CRITICAL)
11df = pd.read_csv('airpassengers.csv')
12
13# 转换数据类型
14df['Month'] = pd.to_datetime(df['Month'], format='%Y-%m')
15 
16# 重命名列
17df = df.rename(columns={'Month': 'ds', 'Passengers': 'y'})
18
19# 使用 Prophet 模型
20model = Prophet()
21model.add_country_holidays(country_name='US')
22model.fit(df)
23
24future = model.make_future_dataframe(periods=24)
25predictions = model.predict(future)
26
27# 绘制各组成部分
28fig, ax = plt.subplots(figsize=(7, 4.5), dpi=300)
29fig = model.plot_components(predictions)
30
31fig.subplots_adjust(top=0.95)
32
33fig.savefig("output/output.png")
34plt.close(fig)

1import pandas as pd
2import matplotlib.pyplot as plt
3from prophet import Prophet
4import datetime
5import logging
6
7# 禁用日志输出
8logger = logging.getLogger('cmdstanpy')
9logger.addHandler(logging.NullHandler())
10logger.propagate = False
11logger.setLevel(logging.CRITICAL)
12df = pd.read_csv('microsoft_stock.csv')
13
14# 转换数据类型
15df["Date"] = pd.to_datetime(df['Date'], format='%m/%d/%Y %H:%M:%S').dt.date
16 
17# 重命名列
18df = df.rename(columns={'Date': 'ds', 'Close': 'y'})
19
20# 使用 Prophet 模型
21model = Prophet()
22model.add_country_holidays(country_name='US')
23model.fit(df)
24
25future = model.make_future_dataframe(periods=180)
26predictions = model.predict(future)
27
28# 绘图
29fig, ax = plt.subplots(figsize=(7, 4.5), dpi=300)
30fig = model.plot(predictions, xlabel='日期', ylabel='价格')
31
32fig.subplots_adjust(top=0.95)
33plt.title("微软股票收盘价预测")
34plt.legend()
35
36fig.savefig("output/output.png")
37plt.close(fig)