需要使用SARIMA模型改善Python现金流量预测的帮助

如何解决需要使用SARIMA模型改善Python现金流量预测的帮助

我正在使用SARIMAX模型在Python中建立每周现金流量预测，但对结果不满意。我正在使用自动机来查找SARIMA的最佳订单和季节性订单。我有过去5年以上的数据，足以建立一个好的模型。我的数据看起来像是附件Historical Data 分解为freq = 7的结果如下statsmodel decompostion

最佳模型：SARIMAX（1,1）（2,1,0）[52]

Forecast Result 我们的预测的均方误差为 4625364095.19 我们的预测的均方根误差为 68010.03

Prediction Steps=25

RMSE太高，因此需要寻求帮助以改善模型性能。快速响应。

我的代码如下：

actual = [35592.63,111814.61,164527.43,136719.53,130048.37,66672.31,151650.05,98633.68,218984.49,32640.38,119842.40,114052.16,78411.80]
dt = pd.date_range("20140113","20200608",freq='W-MON')
df2 = pd.read_csv('mse_ar_data.csv')
df2.index=dt
df2 = np.ceil(df2)
df2

stepwise_fit = auto_arima(df2,start_p = 1,start_q = 1,max_p = 5,max_q = 5,m = 52,start_P = 0,seasonal = True,d = None,D = 1,trace = True,error_action ='ignore',# we don't want to know if an order does not work 
                          suppress_warnings = True,# we don't want convergence warnings 
                          stepwise = True)

stepwise_fit.summary()

model = sm.tsa.statespace.SARIMAX(df2,order=(1,1),seasonal_order=(2,52),enforce_stationarity=False,enforce_invertibility=False)
results_ar = model.fit()
print(results_ar.summary().tables[1])

#Diagnostic Plot
results_ar.plot_diagnostics(figsize=(16,8))
plt.show()

#Prediction
pred_ar = results_ar.get_prediction(start=pd.to_datetime('2020-03-02'),dynamic=False)
pred_ar_ci = pred_ar.conf_int()
ax = df2['2016-01':].plot(label='observed')
pred_ar.predicted_mean.plot(ax=ax,label='One-step ahead Forecast',alpha=.7,figsize=(14,7))
ax.fill_between(pred_ar_ci.index,pred_ar_ci.iloc[:,0],1],color='k',alpha=.2)
ax.set_xlabel('Date')
ax.set_ylabel('Cash Inflow AR')
plt.legend()
plt.show()

y_forecasted =  pred_ar.predicted_mean
y_truth = df2['2020-03-02':]['ar_amount']
mse = ((y_forecasted - y_truth) ** 2).mean()
print('\nThe Mean Squared Error of our forecasts is {}'.format(round(mse,2)))
print('The Root Mean Squared Error of our forecasts is {}'.format(round(np.sqrt(mse),2)))

forcast_ar = pd.DataFrame({'Actual':actual,'Forecasted':pred_ar_uc.predicted_mean})
forcast_ar = forcast_ar.round(2)
forcast_ar['Delta'] = forcast_ar['Forecasted']-forcast_ar['Actual']
print(forcast_ar)

total_delta = round(np.abs(forcast_ar.Delta).sum(),2)
avg_delta = round(np.abs(forcast_ar.Delta).mean(),2)
print('\nTotal Delta:',total_delta)
print('Average Delta:',avg_delta)