[英]High error ARIMA model -count and continues values - Python
我有一個時間序列數據。 它有每日頻率。
我想用 ARIMA model 預測下周或下個月的數據。
這是我的時間序列數據的圖表:
首先,我使用stats model 中的方法seasonal_decompose 來檢查趨勢/會話性/殘差,如下所示:
from statsmodels.tsa.seasonal import seasonal_decompose
result = seasonal_decompose(df['n_transactions'], model='add')
result.plot();
我檢查我的數據是否是固定的:
from statsmodels.tsa.stattools import adfuller
def adf_test(series,title=''):
"""
Pass in a time series and an optional title, returns an ADF report
"""
print(f'Augmented Dickey-Fuller Test: {title}')
result = adfuller(series.dropna(),autolag='AIC') # .dropna() handles differenced data
labels = ['ADF test statistic','p-value','# lags used','# observations']
out = pd.Series(result[0:4],index=labels)
for key,val in result[4].items():
out[f'critical value ({key})']=val
print(out.to_string()) # .to_string() removes the line "dtype: float64"
if result[1] <= 0.05:
print("Strong evidence against the null hypothesis")
print("Reject the null hypothesis")
print("Data has no unit root and is stationary")
else:
print("Weak evidence against the null hypothesis")
print("Fail to reject the null hypothesis")
print("Data has a unit root and is non-stationary")
adf_test(df['n_transactions'])
Augmented Dickey-Fuller Test:
ADF test statistic -3.857922
p-value 0.002367
# lags used 12.000000
# observations 737.000000
critical value (1%) -3.439254
critical value (5%) -2.865470
critical value (10%) -2.568863
Strong evidence against the null hypothesis
Reject the null hypothesis
Data has no unit root and is stationary
我使用 auto_arima 來獲得 model 的最佳參數:
from pmdarima import auto_arima
auto_arima(df['n_transactions'],seasonal=True, m = 7).summary()
我用這個參數訓練我的 model:
train = df.loc[:'2020-05-12']
test = df.loc['2020-05-13':]
model = SARIMAX(train['n_transactions'],order=(1, 1, 1))
results = model.fit()
results.summary()
我計算預測:
start=len(train)
end=len(train)+len(test)-1
predictions = results.predict(start=start, end=end, dynamic=False, typ='levels').rename('SARIMA(0,1,3)(1,0,1,12) Predictions')
ax = test['n_transactions'].plot(legend=True,figsize=(12,6),title=title)
predictions.plot(legend=True)
ax.autoscale(axis='x',tight=True)
ax.set(xlabel=xlabel, ylabel=ylabel);
但是model卻不能取得好的效果,為什么?
正如您建議的那樣,我使用而不是計算我為此獲得的收入,這可能是問題所在:
但是 model 並沒有獲得好的結果:
我可以從這里得出什么結論?
您的數據(無論是計數還是收入)都不太適合 SARIMAX model,原因如下:
相比之下,SARIMAX model 具有同方差高斯誤差,不滿足任何這些特征。 因此,SARIMAX 模型的預測不會很好。
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