写一个用ARIMA时间序列模型对给定数据年份=[2017,2018,2019,2020,2021], 生产总值=[9846.9366,10745.4582,11985.1,12408.66,14594.92], 固定资产投资=[5009.58,5189.92,5610.31,5918.88,6569.95], 社会消费品零售总额=[4047.80935,4154.9262,4473.7247,4238.26249,4649.10761], 出口总额=[735.34,841.68,866.03,924.46,1179.7] 中生产总值进行预测的代码。

代码如下：from statsmodels.tsa.arima_model import ARIMA
import numpy as np# 加载数据
data = np.array([9846.9366,10745.4582,11985.1,12408.66,14594.92])# 构建ARIMA模型
model = ARIMA(data, order=(2,1,2))
model_fit = model.fit(disp=0)# 预测
predictions = model_fit.forecast(steps=1)[0]
print("预测结果：", predictions)

相关问题

写一个用ARIMA时间序列模型对给定数据(年份=[2017,2018,2019,2020,2021], 生产总值=[9846.9366,10745.4582,11985.1,12408.66,14594.92], 固定资产投资=[5009.58,5189.92,5610.31,5918.88,6569.95], 社会消费品零售总额=[4047.80935,4154.9262,4473.7247,4238.26249,4649.10761], 出口总额=[735.34,841.68,866.03,924.46,1179.7]) 中2022年生产总值进行预测的代码。

这里有一段示例代码，你可以参考：from statsmodels.tsa.arima_model import ARIMA
import numpy as np# 准备数据
data = np.array([9846.9366,10745.4582,11985.1,12408.66,14594.92,5009.58,5189.92,5610.31,5918.88,6569.95,4047.80935,4154.9262,4473.7247,4238.26249,4649.10761,735.34,841.68,866.03,924.46,1179.7])# 用ARIMA模型对数据进行拟合
model = ARIMA(data, order=(5,1,0))
model_fit = model.fit(disp=0)# 预测2022年的生产总值
prediction = model_fit.forecast(steps=1)[0]
print("2022年的生产总值预计为：", prediction)

写一个用ARMA时间序列模型对给定数据年份=[2017,2018,2019,2020,2021], 生产总值=[9846.9366,10745.4582,11985.1,12408.66,14594.92], 固定资产投资=[5009.58,5189.92,5610.31,5918.88,6569.95], 社会消费品零售总额=[4047.80935,4154.9262,4473.7247,4238.26249,4649.10761], 出口总额=[735.34,841.68,866.03,924.46,1179.7] 进行预测的代码。

这是一个使用ARMA时间序列模型来预测给定数据的代码：import numpy as np
from statsmodels.tsa.arima_model import ARMA# 设置数据
data = np.array([9846.9366,10745.4582,11985.1,12408.66,14594.92])# 建模
model = ARMA(data, order=(2, 2))
model_fit = model.fit(trend='c', full_output=True, disp=True)# 预测
prediction = model_fit.forecast(steps=5)[0]print("预测值：", prediction)