def predict(pm,periods): future = pm.make_future_dataframe(periods) pm_forecast = pm.predict(future) return pm_forecast 1. 2. 3. 4. 5. #模型评估,采用与测试集的MAPE进行评估 def MAPE(data_true, data_pre):#平均百分比误差率评估 data_true, data_pre = np.array(data_true), np.array(d...
future=model.make_future_dataframe(periods=30)# 创建未来数据框forecast=model.predict(future)# 进行预测 1. 2. 6. 可视化结果 最后,我们将结果可视化,帮助我们更直观地理解未来的预测: # 绘制预测结果fig=model.plot(forecast)# 绘制图形plt.title('Forecast using Prophet')# 添加标题plt.xlabel('Date')# ...
prophet_pred = m.predict(future) prophet_pred.tail() prophet_pred = pd.DataFrame({"Date" : prophet_pred[-12:]['ds'], "Pred" : prophet_pred[-12:]["yhat"]}) prophet_pred = prophet_pred.set_index("Date") prophet_pred.index.freq = "MS" prophet_pred test_data["Prophet_Predictions...
m0_future = m0.predict(future) m0_future.tail() fig= m0.plot(m0_future) a = add_changepoints_to_plot(fig.gca(), m0, m0_future) 知乎学术咨询: https://www.zhihu.com/consult/people/792359672131756032?isMe=1 担任《Mechanical System and Signal Processing》《中国电机工程学报》等期刊审稿专家...
model.predict(future) # Filter the predictions for December 2014 december_forecast = forecast[(forecast['ds'] >= '2014-12-01') & (forecast['ds'] < '2015-01-01')] # Evaluate the model mape = mean_absolute_percentage_error(test['y'], december_forecast['yhat']) mae = mean_absolute...
import prophet m = Prophet() m.fit(df) 未来数据生成与预测 使用make_future_dataframe函数生成未来一段时间的数据框,然后使用拟合好的模型进行预测。 future = m.make_future_dataframe(periods = 30, freq = 'D') forecast = m.predict(future) ...
from fbprophet import Prophet # 初始化Prophet模型 model = Prophet(yearly_seasonality=True) # 训练模型 model.fit(df[['Datetime', 'Traffic_Volume']].rename(columns={'Datetime': 'ds', 'Traffic_Volume': 'y'})) # 生成未来60天的数据框 future = model.make_future_dataframe(periods=60) # 进...
fromfbprophetimportProphet# 初始化Prophet模型model=Prophet(yearly_seasonality=True)# 训练模型model.fit(df[['Datetime','Traffic_Volume']].rename(columns={'Datetime':'ds','Traffic_Volume':'y'}))# 生成未来60天的数据框future=model.make_future_dataframe(periods=60)# 进行预测forecast=model.predict(...
创建Prophet对象,并使用准备好的数据进行拟合。 import prophetm = Prophet()m.fit(df) 未来数据生成与预测 使用make_future_dataframe函数生成未来一段时间的数据框,然后使用拟合好的模型进行预测。 future = m.make\_future\_dataframe(periods = 30, freq = 'D')forecast = m.predict(future) ...
import scikitplot as skplt y_pred = iris_model.predict(X_test).argmax(axis=1) skplt.metrics.plot_confusion_matrix( y_test.argmax(axis=1), y_pred, normalize=True ) 同样地,我们归一化矩阵,以便得到分数。输出应该类似于以下内容: 这比我们在 scikit-learn 中之前的尝试稍逊一筹,但通过一些调...