# 使用当前参数拟合 SARIMA 模型 model = SARIMAX(train['产量(万辆)'], order=param, seasonal_order=(1, 1, 1, 12)) result = model.fit() # 计算模型的均方根误差(RMSE) predicted_sales = result.predict(start=train.index[0], end=train.index[-1]) rmse = np.sqrt(mean_squared_error(trai...
使用SARIMAX 类进行建模。 # 定义参数p=1# 自回归项d=1# 差分次数q=1# 移动平均项seasonal_order=(1,1,1,12)# 季节性参数# 拟合模型model=SARIMAX(data['value_column'],order=(p,d,q),seasonal_order=seasonal_order)results=model.fit()print(results.summary()) 1. 2. 3. 4. 5. 6. 7. 8....
lstm_model.add(LSTM(4, input_shape=(look_back, 1))) lstm_model.add(Dense(1)) lstm_model.compile(loss='mean_squared_error', optimizer='adam') lstm_model.fit(train_X, train_Y, epochs=100, batch_size=1, verbose=0) # LSTM模型预测整个训练集的残差值 lstm_residuals = lstm_model.predic...
# 遍历参数组合,训练 SARIMA 模型并记录 AIC, SC 和调整 2 for params in param_combinations: try: # 定义 SARIMA 模型 model = SARIMAX(dta, order=(params[0], d, params[1]), seasonal_order=(params[2], D, params[3], s), enforce_stationarity=False, enforce_invertibility=False) # 拟合模型...
python # 示例参数 p = 1 d = 1 q = 1 P = 1 D = 1 Q = 1 m = 12 # 假设季节性周期为12个月 # 训练SARIMA模型 model = SARIMAX(df['value'], order=(p, d, q), seasonal_order=(P, D, Q, m)) results = model.fit() 评估和调整模型参数以达到最优预测效果: 你可以使用模型的...
The SARIMA time series forecasting method is supported in Python via the Statsmodels library. To use SARIMA there are three steps, they are: Define the model. Fit the defined model. Make a prediction with the fit model. Let’s look at each step in turn. 1. Define Model An instance of ...
以下是使用Python库statsmodels进行ARIMA建模的示例: import pandas as pd from statsmodels.tsa.arima.model import ARIMA import matplotlib.pyplot as plt # 假设有一个时序数据集,加载数据 data = pd.read_csv('time_series_data.csv', index_col='Date', parse_dates=True) ...
一文速学数模-季节性时序预测SARIMA模型详解+Python实现 1.数据预处理 根据建模步骤我们首先对时间序列数据进行平稳性校验和季节性差分等操作。如果数据不平稳,需要进行差分操作使其变为平稳时间序列。同时,如果数据具有季节性,需要对其进行季节性差分,消除季节性影响。
model = sm.tsa.SARIMAX(ts_train, order=(p, d, q), #enforce_stationarity=False, #enforce_invertibility=False, ) results = model.fit()##下面可以显示具体的参数结果表 ## print(model_results.summary()) ## print(model_results.summary().tables[1]) ...
```python from statsmodels.tsa.statespace.sarimax import SARIMAX #拟合SARIMA模型 model = SARIMAX(data, order=(1, 0, 1), seasonal_order=(1, 1, 1, 12)) model_fit = model.fit() #进行预测 forecast = model_fit.predict(start=len(data), end=len(data)+3) #预测未来4个月的销售量 print...