forecast create y_forecast
时间: 2023-09-28 16:04:01 浏览: 67
`forecast create y_forecast` 是一个 Stata 命令,用于创建一个新的数据集来存储时间序列的预测结果。具体来说,它会在当前数据集中创建一个新的变量 `y_forecast`,并将预测结果存储在其中。
在使用 `forecast create` 命令时,需要指定以下几个参数:
- `replace`:如果数据集中已经存在名为 `y_forecast` 的变量,是否替换它。如果设置了 `replace` 参数,则会替换;如果没有设置,则会提示是否替换。
- `horizon()`:需要预测的未来时间段长度,单位可以是月、季度或年。例如,`horizon(12)` 表示预测未来 12 个月的数据。
因此,`forecast create y_forecast, replace horizon(12)` 表示创建一个名为 `y_forecast` 的变量来存储未来 12 个月的预测结果,并且如果数据集中已经存在 `y_forecast` 变量,则替换它。
相关问题
how to forecast by logit model in python
To forecast using a logistic regression model in Python, you can follow these steps:
1. Load the necessary libraries:
```
import pandas as pd
import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
```
2. Load your dataset into a pandas DataFrame:
```
data = pd.read_csv("dataset.csv")
```
3. Split your dataset into training and testing sets:
```
X_train, X_test, y_train, y_test = train_test_split(data.drop("target", axis=1), data["target"], test_size=0.2)
```
4. Create an instance of the logistic regression model:
```
logit_model = LogisticRegression()
```
5. Fit the model to the training data:
```
logit_model.fit(X_train, y_train)
```
6. Use the model to make predictions on the testing data:
```
predictions = logit_model.predict(X_test)
```
7. Evaluate the performance of the model:
```
accuracy = np.mean(predictions == y_test)
```
8. Use the model to make forecasts on new data:
```
new_data = pd.read_csv("new_data.csv")
new_predictions = logit_model.predict(new_data)
```
Note that the above example assumes that the target variable is binary (0 or 1). If your target variable has more than two possible values, you will need to use a different type of model, such as a multinomial logistic regression.
def machine_study_forecast(df): df1=clean_date(df) middle_time = pd.to_datetime('2014-12-14') df1['date'] = pd.to_datetime(df1['date']) df2 = df1[df1['date'] > middle_time] df3 = df1[df1['date'] <= middle_time] df_train=pd.get_dummies(df3['behavior_type'],prefix='behavior_type') print(df_train) y=df_train.behavior_type_4.values y = y.reshape(-1, 1) x=df_train.drop(columns=['behavior_type_4']) x=pd.concat([df3,x],axis=1) x=x.drop(columns=['behavior_type']) print(x) print(y) x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=0) ann = Sequential() # 创建一个序贯ANN(Artifical Neural Network)模型 ann.add(Dense(units=8, input_dim=8, activation='relu')) # 添加输入层 ann.add(Dense(units=16, activation='relu')) # 添加隐层 ann.add(Dense(units=1, activation='sigmoid')) # 添加输出层 ann.summary() # 显示网络模型(这个语句不是必须的) # SVG(model_to_dot(ann, show_shapes=True).create(prog='dot', format='svg')) ann.compile(optimizer='adam', # 优化器 loss='binary_crossentropy', # 损失函数 metrics=['acc']) # 评估指标 history = ann.fit(x_train, y_train, # 指定训练集 epochs=30, # 指定训练的轮次 batch_size=64, # 指定数据批量 validation_data=(x_test, y_test)) # 指定验证集,这里为了简化模型,直接用测试集数据进行验证
这是一段Python代码,用于进行机器学习预测。其中的步骤包括数据清理、数据处理(如对日期进行转换)、对数据进行编码、制定输入和输出变量,并对数据进行训练和测试。最后用到了Keras深度学习框架中的Sequential类。
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