self._add_new_data(self.data_ts, dat, type) if self.data_ts.index[-1] != self.predict_ts.index[-1]: raise ValueError('You must use the forecast_next_day_value method forecast the value of today before') self._add_new_data(self.resid_ts, self.data_ts[-1] - self.predict_ts[...
seasonal_order=(param[2], D,param[3], s)).fit(disp=-1)except:continueaic = model.aic# saving best model, AIC and parametersifaic< best_aic: best_model = model best_aic = aic best_param = param results.append([param, model.aic]) result_table = pd.DataFrame(results) result_table....
model_fit = model.fit(disp=0) residuals = model_fit.residelse: model =ARIMA(series, dates=dates, order=order) model_fit = model.fit(disp=0) residuals = model_fit.residreturnmodel_fit, residuals 开发者ID:shubhomoydas,项目名称:ad_examples,代码行数:28,代码来源:timeseries_arima.py 示例9:...
fit(disp=-1) print(fitted.summary()) # Forecast fc, se, conf = fitted.forecast(15, alpha=0.05) # 95% conf # Make as pandas series fc_series = pd.Series(fc, index=test.index) lower_series = pd.Series(conf[:, 0], index=test.index) upper_series = pd.Series(conf[:, 1], ...
eadModel = fitEADModel(EADData(TrainingInd,:),ModelType,PredictorVars={'UtilizationRate','Age','Marriage'},...ConversionMeasure=ConversionMeasure,DrawnVar="Drawn",LimitVar="Limit",ResponseVar="EAD"); disp(eadModel); Tobit with properties: CensoringSide: "both" LeftLimit: 0 RightLimit: 1 Mo...
model=sm.tsa.statespace.SARIMAX(train_pd['PUISSANCE'],order=(5,1,5),exog=train_pd[['JOUR_SEMAINE', 'Jour_F', 'HEURE_CREUSE', 'HEURE_POINTE']]) results = model.fit() results.summary() If the problem persists, you can try checking the data types of the columns in thetrain...
1. start:设置的起始值 步骤4:拟合模型 接下来,我们需要拟合我们修改过的模型。 res=model.fit(disp='off') 1. disp:显示拟合的信息 步骤5:查看结果 最后,我们可以查看拟合模型的结果。 print(res.summary()) 1. 类图 arch_model- data- vol- p- q+fix(start)+fit(disp) ...
Display model fit summary of merMod objects, fastmerMod
lgdModel = fitLGDModel(data(TrainingInd,:),'tobit'); disp(lgdModel) Tobit with properties: CensoringSide: "both" LeftLimit: 0 RightLimit: 1 Weights: [0x1 double] ModelID: "Tobit" Description: "" UnderlyingModel: [1x1 risk.internal.credit.TobitModel] PredictorVars: ["LTV" "Age" "Type...
I used Add and LayerNormalization layers at the same time. The fit works well. But when it comes to predict or anything else, it will occur errors. Detail is below. Hint: If I use with tf.device('/cpu:0'): before predict, it is good. Idon't know why the prediction can't work...