import pandas as pdimport numpy as npimport matplotlib.pyplot as pltfrom statsmodels.tsa.arima.model import ARIMAfrom sklearn.metrics import mean_squared_error# 读取csv文件data = pd.read_csv("data.csv", header=None)# 划分训练集和测试集train_size = int(len(data) * 0.7)train_data, test_data = data[:train_size], data[train_size:]# 将时间编号设置为索引train_data.set_index(0, inplace=True)test_data.set_index(0, inplace=True)# 转换为时间序列train_ts = train_data[1]test_ts = test_data[1]# 训练ARIMA模型model = ARIMA(train_ts, order=(1, 1, 1))model_fit = model.fit()# 预测测试集数据predictions = model_fit.forecast(steps=len(test_ts))[0]# 计算rmsermse = np.sqrt(mean_squared_error(test_ts, predictions))print('Test RMSE: %.3f' % rmse)# 绘制图像plt.figure(figsize=(10, 6))plt.plot(train_ts, label='Train')plt.plot(test_ts, label='Test')plt.plot(test_ts.index, predictions, label='Predictions')plt.xlabel('Time/h')plt.ylabel('kwh')plt.title('ARIMA Model Predictions')plt.legend()plt.show()以上代码运行报错如下ValueError: 0 is not in range，请修正代码

在将时间编号设置为索引的时候，需要将原来的索引列删除并使用 `set_index` 方法设置新的索引列。修改代码如下：

# 读取csv文件
data = pd.read_csv("data.csv", header=None)

# 划分训练集和测试集
train_size = int(len(data) * 0.7)
train_data, test_data = data[:train_size], data[train_size:]

# 将时间编号设置为索引
train_data.set_index(0, inplace=True)
test_data.set_index(0, inplace=True)

# 删除原来的索引列
train_data.index.name = None
test_data.index.name = None

# 转换为时间序列
train_ts = train_data[1]
test_ts = test_data[1]

# 训练ARIMA模型
model = ARIMA(train_ts, order=(1, 1, 1))
model_fit = model.fit()

# 预测测试集数据
predictions = model_fit.forecast(steps=len(test_ts))[0]

# 计算rmse
rmse = np.sqrt(mean_squared_error(test_ts, predictions))
print('Test RMSE: %.3f' % rmse)

# 绘制图像
plt.figure(figsize=(10, 6))
plt.plot(train_ts, label='Train')
plt.plot(test_ts, label='Test')
plt.plot(test_ts.index, predictions, label='Predictions')
plt.xlabel('Time/h')
plt.ylabel('kwh')
plt.title('ARIMA Model Predictions')
plt.legend()
plt.show()

这样就可以解决报错了。