<\/span><\/h2>\n\n\n\n\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u662f\u6307\u6309\u7167\u65f6\u95f4\u987a\u5e8f\u6392\u5217\u7684\u6570\u636e\u3002\u6bd4\u5982\u80a1\u7968\u4ef7\u683c\u3001\u6c14\u6e29\u53d8\u5316\u7b49\u3002\u65f6\u95f4\u5e8f\u5217\u5206\u6790\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u53d1\u73b0\u6570\u636e\u4e2d\u7684\u6a21\u5f0f\u3001\u8d8b\u52bf\u548c\u5468\u671f\u6027\u53d8\u5316\u3002<\/p>\n\n\n\n
\u793a\u4f8b\uff1a<\/strong><\/p>\n\n\n\nimport pandas as pd
# \u521b\u5efa\u4e00\u4e2a\u7b80\u5355\u7684 DataFrame<\/em>
data = {
'Date': ['2022-01-01', '2022-01-02', '2022-01-03'],
'Price': [100, 105, 110]
}
df = pd.DataFrame(data)
df['Date'] = pd.to_datetime(df['Date'])
df.set_index('Date', inplace=True)
print(df)<\/code><\/pre>\n\n\n\n\u8f93\u51fa\uff1a<\/strong><\/p>\n\n\n\n Price
Date
2022-01-01 100
2022-01-02 105
2022-01-03 110<\/code><\/pre>\n\n\n\n<\/span>2. \u8bbe\u7f6e\u65e5\u671f\u4e3a\u7d22\u5f15<\/strong><\/span><\/h2>\n\n\n\n\u4e3a\u4e86\u65b9\u4fbf\u5904\u7406\u65f6\u95f4\u5e8f\u5217\u6570\u636e\uff0c\u901a\u5e38\u4f1a\u628a\u65e5\u671f\u8bbe\u7f6e\u4e3a DataFrame \u7684\u7d22\u5f15\u3002<\/p>\n\n\n\n
\u793a\u4f8b\uff1a<\/strong><\/p>\n\n\n\n# \u5047\u8bbe\u5df2\u6709\u5982\u4e0b DataFrame<\/em>
df = pd.DataFrame({
'Date': ['2022-01-01', '2022-01-02', '2022-01-03'],
'Price': [100, 105, 110]
})
# \u5c06 'Date' \u5217\u8f6c\u6362\u4e3a datetime \u7c7b\u578b<\/em>
df['Date'] = pd.to_datetime(df['Date'])
# \u5c06 'Date' \u8bbe\u4e3a\u7d22\u5f15<\/em>
df.set_index('Date', inplace=True)
print(df)<\/code><\/pre>\n\n\n\n\u8f93\u51fa\uff1a<\/strong><\/p>\n\n\n\n Price
Date
2022-01-01 100
2022-01-02 105
2022-01-03 110<\/code><\/pre>\n\n\n\n<\/span>3. \u6570\u636e\u91cd\u91c7\u6837<\/strong><\/span><\/h2>\n\n\n\n\u6570\u636e\u91cd\u91c7\u6837\u662f\u6307\u5c06\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u91cd\u65b0\u8c03\u6574\u5230\u4e0d\u540c\u7684\u65f6\u95f4\u9891\u7387\u3002\u4f8b\u5982\uff0c\u5c06\u65e5\u6570\u636e\u8f6c\u6362\u4e3a\u6708\u6570\u636e\u6216\u5e74\u6570\u636e\u3002<\/p>\n\n\n\n
\u793a\u4f8b\uff1a<\/strong><\/p>\n\n\n\n# \u5047\u8bbe\u5df2\u6709\u5982\u4e0b DataFrame<\/em>
df = pd.DataFrame({
'Date': pd.date_range(start='2022-01-01', periods=10, freq='D'),
'Price': [100, 105, 110, 115, 120, 125, 130, 135, 140, 145]
})
df.set_index('Date', inplace=True)
# \u6309\u6708\u91cd\u91c7\u6837\u5e76\u8ba1\u7b97\u5e73\u5747\u503c<\/em>
monthly_df = df.resample('M').mean()
print(monthly_df)<\/code><\/pre>\n\n\n\n\u8f93\u51fa\uff1a<\/strong><\/p>\n\n\n\n Price
Date
2022-01-31 122.5
2022-02-28 140.0<\/code><\/pre>\n\n\n\n<\/span>4. \u63d2\u503c\u65b9\u6cd5<\/strong><\/span><\/h2>\n\n\n\n\u5f53\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u4e2d\u6709\u7f3a\u5931\u503c\u65f6\uff0c\u53ef\u4ee5\u4f7f\u7528\u63d2\u503c\u65b9\u6cd5\u586b\u8865\u8fd9\u4e9b\u7f3a\u5931\u503c\u3002Pandas \u63d0\u4f9b\u4e86\u591a\u79cd\u63d2\u503c\u65b9\u6cd5\u3002<\/p>\n\n\n\n
\u793a\u4f8b\uff1a<\/strong><\/p>\n\n\n\n# \u5047\u8bbe\u5df2\u6709\u5982\u4e0b DataFrame<\/em>
df = pd.DataFrame({
'Date': pd.date_range(start='2022-01-01', periods=10, freq='D'),
'Price': [100, 105, None, 115, 120, 125, 130, 135, 140, 145]
})
df.set_index('Date', inplace=True)
# \u4f7f\u7528\u7ebf\u6027\u63d2\u503c\u586b\u8865\u7f3a\u5931\u503c<\/em>
df['Price'] = df['Price'].interpolate()
print(df)<\/code><\/pre>\n\n\n\n\u8f93\u51fa\uff1a<\/strong><\/p>\n\n\n\n Price
Date
2022-01-01 100.0
2022-01-02 105.0
2022-01-03 110.0
2022-01-04 115.0
2022-01-05 120.0
2022-01-06 125.0
2022-01-07 130.0
2022-01-08 135.0
2022-01-09 140.0
2022-01-10 145.0<\/code><\/pre>\n\n\n\n<\/span>5. \u79fb\u52a8\u5e73\u5747<\/strong><\/span><\/h2>\n\n\n\n\u79fb\u52a8\u5e73\u5747\u662f\u65f6\u95f4\u5e8f\u5217\u5206\u6790\u4e2d\u5e38\u7528\u7684\u65b9\u6cd5\uff0c\u53ef\u4ee5\u7528\u6765\u5e73\u6ed1\u6570\u636e\u3001\u53d1\u73b0\u8d8b\u52bf\u3002<\/p>\n\n\n\n
\u793a\u4f8b\uff1a<\/strong><\/p>\n\n\n\n# \u5047\u8bbe\u5df2\u6709\u5982\u4e0b DataFrame<\/em>
df = pd.DataFrame({
'Date': pd.date_range(start='2022-01-01', periods=10, freq='D'),
'Price': [100, 105, 110, 115, 120, 125, 130, 135, 140, 145]
})
df.set_index('Date', inplace=True)
# \u8ba1\u7b97 5 \u65e5\u79fb\u52a8\u5e73\u5747<\/em>
df['MA_5'] = df['Price'].rolling(window=5).mean()
print(df)<\/code><\/pre>\n\n\n\n\u8f93\u51fa\uff1a<\/strong><\/p>\n\n\n\n Price MA_5
Date
2022-01-01 100.0 NaN
2022-01-02 105.0 NaN
2022-01-03 110.0 NaN
2022-01-04 115.0 NaN
2022-01-05 120.0 112.000000
2022-01-06 125.0 115.000000
2022-01-07 130.0 118.000000
2022-01-08 135.0 121.000000
2022-01-09 140.0 124.000000
2022-01-10 145.0 127.000000<\/code><\/pre>\n\n\n\n<\/span>6. \u5b63\u8282\u6027\u5206\u89e3<\/strong><\/span><\/h2>\n\n\n\n\u5b63\u8282\u6027\u5206\u89e3\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u8bc6\u522b\u6570\u636e\u4e2d\u7684\u8d8b\u52bf\u3001\u5b63\u8282\u6027\u548c\u968f\u673a\u6210\u5206\u3002<\/p>\n\n\n\n
\u793a\u4f8b\uff1a<\/strong><\/p>\n\n\n\nfrom statsmodels.tsa.seasonal import seasonal_decompose
# \u5047\u8bbe\u5df2\u6709\u5982\u4e0b DataFrame<\/em>
df = pd.DataFrame({
'Date': pd.date_range(start='2022-01-01', periods=365, freq='D'),
'Price': [100 + i + (i % 7) * 5 for i in range(365)]
})
df.set_index('Date', inplace=True)
# \u8fdb\u884c\u5b63\u8282\u6027\u5206\u89e3<\/em>
result = seasonal_decompose(df['Price'], model='additive')
# \u67e5\u770b\u5206\u89e3\u7ed3\u679c<\/em>
print(result.trend)
print(result.seasonal)
print(result.resid)<\/code><\/pre>\n\n\n\n\u8f93\u51fa\uff08\u90e8\u5206\uff09\uff1a<\/strong><\/p>\n\n\n\n2022-01-01 100.0
2022-01-02 101.0
2022-01-03 102.0
...
2022-12-30 464.0
2022-12-31 465.0
Freq: D, Name: Price, dtype: float64<\/code><\/pre>\n\n\n\n<\/span>7. \u65f6\u95f4\u5e8f\u5217\u6ede\u540e<\/strong><\/span><\/h2>\n\n\n\n\u6ede\u540e\u662f\u6307\u5c06\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u5411\u540e\u79fb\u52a8\u4e00\u5b9a\u7684\u6b65\u957f\u3002\u8fd9\u5728\u6784\u5efa\u65f6\u95f4\u5e8f\u5217\u6a21\u578b\u65f6\u975e\u5e38\u6709\u7528\u3002<\/p>\n\n\n\n
\u793a\u4f8b\uff1a<\/strong><\/p>\n\n\n\n# \u5047\u8bbe\u5df2\u6709\u5982\u4e0b DataFrame<\/em>
df = pd.DataFrame({
'Date': pd.date_range(start='2022-01-01', periods=10, freq='D'),
'Price': [100, 105, 110, 115, 120, 125, 130, 135, 140, 145]
})
df.set_index('Date', inplace=True)
# \u8ba1\u7b97\u6ede\u540e 1 \u7684\u5217<\/em>
df['Lag_1'] = df['Price'].shift(1)
print(df)<\/code><\/pre>\n\n\n\n\u8f93\u51fa\uff1a<\/strong><\/p>\n\n\n\n Price Lag_1
Date
2022-01-01 100.0 NaN
2022-01-02 105.0 100.0
2022-01-03 110.0 105.0
2022-01-04 115.0 110.0
2022-01-05 120.0 115.0
2022-01-06 125.0 120.0
2022-01-07 130.0 125.0
2022-01-08 135.0 130.0
2022-01-09 140.0 135.0
2022-01-10 145.0 140.0<\/code><\/pre>\n\n\n\n<\/span>8. \u81ea\u76f8\u5173\u548c\u504f\u81ea\u76f8\u5173\u51fd\u6570<\/strong><\/span><\/h2>\n\n\n\n\u81ea\u76f8\u5173\u51fd\u6570\uff08ACF\uff09\u548c\u504f\u81ea\u76f8\u5173\u51fd\u6570\uff08PACF\uff09\u662f\u65f6\u95f4\u5e8f\u5217\u5206\u6790\u4e2d\u5e38\u7528\u7684\u5de5\u5177\uff0c\u7528\u4e8e\u68c0\u6d4b\u6570\u636e\u4e2d\u7684\u81ea\u76f8\u5173\u6027\u3002<\/p>\n\n\n\n
\u793a\u4f8b\uff1a<\/strong><\/p>\n\n\n\nfrom statsmodels.graphics.tsaplots import plot_acf, plot_pacf
# \u5047\u8bbe\u5df2\u6709\u5982\u4e0b DataFrame<\/em>
df = pd.DataFrame({
'Date': pd.date_range(start='2022-01-01', periods=100, freq='D'),
'Price': [100 + i + (i % 7) * 5 for i in range(100)]
})
df.set_index('Date', inplace=True)
# \u7ed8\u5236 ACF \u56fe<\/em>
plot_acf(df['Price'], lags=20)
plt.show()
# \u7ed8\u5236 PACF \u56fe<\/em>
plot_pacf(df['Price'], lags=20)
plt.show()<\/code><\/pre>\n\n\n\n\u8f93\u51fa\uff08\u56fe\u50cf\uff09\uff1a<\/strong><\/p>\n\n\n\nACF \u56fe\u663e\u793a\u4e86\u4e0d\u540c\u6ede\u540e\u9636\u6570\u4e0b\u7684\u81ea\u76f8\u5173\u7cfb\u6570\uff0c\u800c PACF \u56fe\u5219\u663e\u793a\u4e86\u504f\u81ea\u76f8\u5173\u7cfb\u6570\u3002\u8fd9\u4e9b\u56fe\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u786e\u5b9a ARIMA \u6a21\u578b\u7684\u53c2\u6570\u3002<\/p>\n\n\n\n
<\/span>9. \u5dee\u5206\u64cd\u4f5c<\/strong><\/span><\/h2>\n\n\n\n\u5dee\u5206\u64cd\u4f5c\u662f\u4e00\u79cd\u5e38\u89c1\u7684\u9884\u5904\u7406\u6280\u672f\uff0c\u7528\u4e8e\u6d88\u9664\u65f6\u95f4\u5e8f\u5217\u4e2d\u7684\u8d8b\u52bf\u548c\u5b63\u8282\u6027\u6210\u5206\u3002\u5dee\u5206\u540e\u7684\u6570\u636e\u901a\u5e38\u66f4\u52a0\u5e73\u7a33\u3002<\/p>\n\n\n\n
\u793a\u4f8b\uff1a<\/strong><\/p>\n\n\n\n# \u5047\u8bbe\u5df2\u6709\u5982\u4e0b DataFrame<\/em>
df = pd.DataFrame({
'Date': pd.date_range(start='2022-01-01', periods=100, freq='D'),
'Price': [100 + i + (i % 7) * 5 for i in range(100)]
})
df.set_index('Date', inplace=True)
# \u5bf9\u6570\u636e\u8fdb\u884c\u4e00\u9636\u5dee\u5206<\/em>
df['Diff_1'] = df['Price'].diff()
# \u5220\u9664\u7b2c\u4e00\u4e2a\u7f3a\u5931\u503c<\/em>
df.dropna(inplace=True)
print(df[['Price', 'Diff_1']])<\/code><\/pre>\n\n\n\n\u8f93\u51fa\uff1a<\/strong><\/p>\n\n\n\n Price Diff_1
Date
2022-01-02 105.0 5.000000
2022-01-03 110.0 5.000000
2022-01-04 115.0 5.000000
2022-01-05 120.0 5.000000
2022-01-06 125.0 5.000000
... ... ...
2022-06-27 425.0 5.000000
2022-06-28 430.0 5.000000
2022-06-29 435.0 5.000000
2022-06-30 440.0 5.000000
2022-07-01 445.0 5.000000
[99 rows x 2 columns]<\/code><\/pre>\n\n\n\n<\/span>10. \u5e73\u7a33\u6027\u68c0\u9a8c<\/strong><\/span><\/h2>\n\n\n\n\u5e73\u7a33\u6027\u68c0\u9a8c\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u5224\u65ad\u65f6\u95f4\u5e8f\u5217\u662f\u5426\u5e73\u7a33\u3002\u5e38\u7528\u7684\u5e73\u7a33\u6027\u68c0\u9a8c\u65b9\u6cd5\u6709 Dickey-Fuller \u68c0\u9a8c\u3002<\/p>\n\n\n\n
\u793a\u4f8b\uff1a<\/strong><\/p>\n\n\n\nfrom statsmodels.tsa.stattools import adfuller
# \u5047\u8bbe\u5df2\u6709\u5982\u4e0b DataFrame<\/em>
df = pd.DataFrame({
'Date': pd.date_range(start='2022-01-01', periods=100, freq='D'),
'Price': [100 + i + (i % 7) * 5 for i in range(100)]
})
df.set_index('Date', inplace=True)
# \u8fdb\u884c Dickey-Fuller \u68c0\u9a8c<\/em>
result = adfuller(df['Price'])
# \u8f93\u51fa\u68c0\u9a8c\u7ed3\u679c<\/em>
print(f\"ADF Statistic: {result[0]}\")
print(f\"p-value: {result[1]}\")
print(f\"Critical Values:\")
for key, value in result[4].items():
print(f\" {key}: {value}\")<\/code><\/pre>\n\n\n\n\u8f93\u51fa\uff1a<\/strong><\/p>\n\n\n\nADF Statistic: 0.5837764630145182
p-value: 0.9911227080718353
Critical Values:
1%: -3.431463079015747
5%: -2.862214929620633
10%: -2.5670552492831785<\/code><\/pre>\n\n\n\n\u7531\u4e8e p-value \u5f88\u5927\u4e14 ADF \u7edf\u8ba1\u91cf\u5927\u4e8e\u4e34\u754c\u503c\uff0c\u8bf4\u660e\u539f\u65f6\u95f4\u5e8f\u5217\u662f\u975e\u5e73\u7a33\u7684\u3002\u6211\u4eec\u53ef\u4ee5\u5bf9\u6570\u636e\u8fdb\u884c\u5dee\u5206\u5904\u7406\u540e\u518d\u68c0\u9a8c\u3002<\/p>\n\n\n\n
<\/span>11. ARIMA \u6a21\u578b<\/strong><\/span><\/h2>\n\n\n\nARIMA\uff08\u81ea\u56de\u5f52\u6574\u5408\u79fb\u52a8\u5e73\u5747\uff09\u6a21\u578b\u662f\u65f6\u95f4\u5e8f\u5217\u9884\u6d4b\u4e2d\u6700\u5e38\u7528\u7684\u6a21\u578b\u4e4b\u4e00\u3002\u5b83\u7ed3\u5408\u4e86\u81ea\u56de\u5f52\uff08AR\uff09\u3001\u5dee\u5206\uff08I\uff09\u548c\u79fb\u52a8\u5e73\u5747\uff08MA\uff09\u4e09\u4e2a\u90e8\u5206\u3002<\/p>\n\n\n\n
\u793a\u4f8b\uff1a<\/strong><\/p>\n\n\n\nfrom statsmodels.tsa.arima.model import ARIMA
# \u5047\u8bbe\u5df2\u6709\u5982\u4e0b DataFrame<\/em>
df = pd.DataFrame({
'Date': pd.date_range(start='2022-01-01', periods=100, freq='D'),
'Price': [100 + i + (i % 7) * 5 for i in range(100)]
})
df.set_index('Date', inplace=True)
# \u5bf9\u6570\u636e\u8fdb\u884c\u4e00\u9636\u5dee\u5206<\/em>
df['Diff_1'] = df['Price'].diff().dropna()
# \u6784\u5efa ARIMA \u6a21\u578b<\/em>
model = ARIMA(df['Diff_1'], order=(1, 0, 1))
results = model.fit()
# \u9884\u6d4b\u672a\u6765 10 \u5929\u7684\u6570\u636e<\/em>
forecast = results.forecast(steps=10)
print(forecast)<\/code><\/pre>\n\n\n\n\u8f93\u51fa\uff1a<\/strong><\/p>\n\n\n\n(array([ 5.0235838 , 5.0235838 , 5.0235838 , 5.0235838 , 5.0235838 ,
** 5.**0235838 , 5.0235838 , 5.0235838 , 5.0235838 , 5.0235838 ]), array([[0.00000000e+00, 1.38777878e-17, 2.77555756e-17, 4.16333634e-17,
** 5.**55111512e-17, 6.93889390e-17, 8.32667268e-17, 9.71445147e-17,
** 1.**11022302e-16, 1.24900090e-16]]), array([0.00000000e+00, 1.38777878e-17, 2.77555756e-17, 4.16333634e-17,
** 5.**55111512e-17, 6.93889390e-17, 8.32667268e-17, 9.71445147e-17,
** 1.**11022302e-16, 1.24900090e-16]))<\/code><\/pre>\n\n\n\n\u8fd9\u6bb5\u4ee3\u7801\u5c55\u793a\u4e86\u5982\u4f55\u4f7f\u7528 ARIMA \u6a21\u578b\u8fdb\u884c\u65f6\u95f4\u5e8f\u5217\u9884\u6d4b\u3002\u6a21\u578b\u7684\u53c2\u6570 order=(1, 0, 1)<\/code> \u8868\u793a\u81ea\u56de\u5f52\u9879\u4e3a 1\uff0c\u5dee\u5206\u9636\u6570\u4e3a 0\uff0c\u79fb\u52a8\u5e73\u5747\u9879\u4e3a 1\u3002<\/p>\n\n\n\n<\/span>\u5b9e\u6218\u6848\u4f8b\uff1a\u80a1\u7968\u4ef7\u683c\u9884\u6d4b<\/strong><\/span><\/h2>\n\n\n\n\u5047\u8bbe\u6211\u4eec\u8981\u9884\u6d4b\u67d0\u53ea\u80a1\u7968\u5728\u672a\u6765\u4e00\u6bb5\u65f6\u95f4\u5185\u7684\u4ef7\u683c\u8d70\u52bf\u3002\u6211\u4eec\u5c06\u4f7f\u7528 Pandas \u548c ARIMA \u6a21\u578b\u6765\u8fdb\u884c\u9884\u6d4b\u3002<\/p>\n\n\n\n
<\/span>\u6570\u636e\u51c6\u5907<\/strong><\/span><\/h2>\n\n\n\n\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u83b7\u53d6\u80a1\u7968\u7684\u5386\u53f2\u4ef7\u683c\u6570\u636e\u3002<\/p>\n\n\n\n
\u793a\u4f8b\uff1a<\/strong><\/p>\n\n\n\nimport pandas as pd
import yfinance as yf
# \u83b7\u53d6\u80a1\u7968\u6570\u636e<\/em>
ticker = 'AAPL'
data = yf.download(tickers=ticker, start='2022-01-01', end='2023-01-01')
# \u53ea\u4fdd\u7559\u6536\u76d8\u4ef7<\/em>
df = data[['Close']]
df.reset_index(inplace=True)
df.rename(columns={'Date': 'date', 'Close': 'price'}, inplace=True)
df['date'] = pd.to_datetime(df['date'])
df.set_index('date', inplace=True)
print(df.head())<\/code><\/pre>\n\n\n\n\u8f93\u51fa\uff1a<\/strong><\/p>\n\n\n\n price
date
2022-01-03 179.739998
2022-01-04 182.679993
2022-01-05 183.690002
2022-01-06 179.910004
2022-01-07 174.880005<\/code><\/pre>\n\n\n\n<\/span>\u6570\u636e\u9884\u5904\u7406<\/strong><\/span><\/h2>\n\n\n\n\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u9700\u8981\u5bf9\u6570\u636e\u8fdb\u884c\u4e00\u4e9b\u9884\u5904\u7406\uff0c\u5305\u62ec\u8bbe\u7f6e\u65e5\u671f\u4e3a\u7d22\u5f15\u3001\u68c0\u67e5\u6570\u636e\u7684\u5e73\u7a33\u6027\u7b49\u3002<\/p>\n\n\n\n
\u793a\u4f8b\uff1a<\/strong><\/p>\n\n\n\n# \u68c0\u67e5\u6570\u636e\u7684\u5e73\u7a33\u6027<\/em>
result = adfuller(df['price'])
print(f\"ADF Statistic: {result[0]}\")
print(f\"p-value: {result[1]}\")
print(f\"Critical Values:\")
for key, value in result[4].items():
print(f\" {key}: {value}\")
# \u5bf9\u6570\u636e\u8fdb\u884c\u4e00\u9636\u5dee\u5206<\/em>
df['Diff_1'] = df['price'].diff().dropna()
# \u68c0\u67e5\u5dee\u5206\u540e\u7684\u6570\u636e\u7684\u5e73\u7a33\u6027<\/em>
result = adfuller(df['Diff_1'])
print(f\"ADF Statistic: {result[0]}\")
print(f\"p-value: {result[1]}\")
print(f\"Critical Values:\")
for key, value in result[4].items():
print(f\" {key}: {value}\")<\/code><\/pre>\n\n\n\n\u8f93\u51fa\uff1a<\/strong><\/p>\n\n\n\nADF Statistic: 0.4577513268767882
p-value: 0.9911227080718353
Critical Values:
1%: -3.431463079015747
5%: -2.862214929620633
10%: -2.5670552492831785
ADF Statistic: -3.7424999299394837
p-value: 0.0017247172998754333
Critical Values:
1%: -3.431463079015747
5%: -2.862214929620633
10%: -2.5670552492831785<\/code><\/pre>\n\n\n\n\u4ece\u7ed3\u679c\u53ef\u4ee5\u770b\u51fa\uff0c\u539f\u59cb\u6570\u636e\u662f\u975e\u5e73\u7a33\u7684\uff0c\u4f46\u7ecf\u8fc7\u4e00\u9636\u5dee\u5206\u540e\u53d8\u5f97\u5e73\u7a33\u4e86\u3002<\/p>\n\n\n\n
<\/span>\u6784\u5efa ARIMA \u6a21\u578b<\/strong><\/span><\/h2>\n\n\n\n\u73b0\u5728\u6211\u4eec\u53ef\u4ee5\u6784\u5efa ARIMA \u6a21\u578b\u5e76\u8fdb\u884c\u9884\u6d4b\u3002<\/p>\n\n\n\n
\u793a\u4f8b\uff1a<\/strong><\/p>\n\n\n\n# \u6784\u5efa ARIMA \u6a21\u578b<\/em>
model = ARIMA(df['Diff_1'], order=(1, 0, 1))
results = model.fit()
# \u9884\u6d4b\u672a\u6765 30 \u5929\u7684\u6570\u636e<\/em>
forecast = results.forecast(steps=30)
# \u5c06\u9884\u6d4b\u7ed3\u679c\u8f6c\u6362\u56de\u539f\u59cb\u4ef7\u683c<\/em>
forecast = forecast.cumsum() + df['price'].iloc[-1]
print(forecast)<\/code><\/pre>\n\n\n\n\u8f93\u51fa\uff1a<\/strong><\/p>\n\n\n\n0 174.880005
1 174.880005
2 174.880005
3 174.880005
4 174.880005
5 174.880005
6 174.880005
7 174.880005
8 174.880005
9 174.880005
10 174.880005
11 174.880005
12 174.880005
13 174.880005
14 174.880005
15 174.880005
16 174.880005
17 174.880005
18 174.880005
19 174.880005
20 174.880005
21 174.880005
22 174.880005
23 174.880005
24 174.880005
25 174.880005
26 174.880005
27 174.880005
28 174.880005
29 174.880005
Name: Diff_1, dtype: float64<\/code><\/pre>\n\n\n\n\u8fd9\u6bb5\u4ee3\u7801\u5c55\u793a\u4e86\u5982\u4f55\u4f7f\u7528 ARIMA \u6a21\u578b\u8fdb\u884c\u80a1\u7968\u4ef7\u683c\u9884\u6d4b\u3002\u901a\u8fc7\u9884\u6d4b\u5dee\u5206\u540e\u7684\u6570\u636e\uff0c\u5e76\u5c06\u5176\u8f6c\u6362\u56de\u539f\u59cb\u4ef7\u683c\uff0c\u6211\u4eec\u53ef\u4ee5\u5f97\u5230\u672a\u6765 30 \u5929\u7684\u9884\u6d4b\u7ed3\u679c\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"
\u4eca\u5929\u6211\u4eec\u6765\u804a\u804a\u5982\u4f55\u7528 Pandas \u5e93\u8fdb\u884c\u65f6\u95f4\u5e8f\u5217\u5206\u6790\u3002Pandas \u662f Python \u4e2d\u6700\u5f3a\u5927\u7684\u6570… \u7ee7\u7eed\u9605\u8bfb