修改以下代码，使其能正常运行： import pandas as pd from statsmodels.tsa.arima.model import ARIMA from pyecharts.charts import Line from pyecharts import options as opts # 读取数据 data1 = pd.read_csv('weather.csv') data2 = pd.read_csv('weatherw.csv') # 将数据合并 data = pd.concat([data1, data2], ignore_index=True) # 将日期转换为时间戳 data['日期'] = pd.to_datetime(data['日期']) # 将数据按日期排序 data = data.sort_values(by='日期') # 将最高气温和最低气温数据转换为列表 high = data['最高气温'].tolist() low = data['最低气温'].tolist() # 建立ARIMA模型，预测2023年每一天的最高气温和最低气温 model_high = ARIMA(high, order=(1, 1, 1)).fit() model_low = ARIMA(low, order=(1, 1, 1)).fit() predict_high = model_high.predict(start=len(high), end=len(high) + 364, typ='levels') predict_low = model_low.predict(start=len(low), end=len(low) + 364, typ='levels') # 将预测结果转换为DataFrame格式 predict = pd.DataFrame({ '日期': pd.date_range(start='2023-01-01', end='2023-12-31'), '最高气温': predict_high, '最低气温': predict_low }) # 将预测结果保存到文件中 predict.to_csv('predict.csv', index=False) # 绘制折线图 line = Line() line.add_xaxis(predict['日期'].dt.strftime('%Y-%m-%d').tolist()) line.add_yaxis('最高气温', predict['最高气温'].tolist()) line.add_yaxis('最低气温', predict['最低气温'].tolist()) line.set_global_opts(title_opts=opts.TitleOpts(title='2023年气温预测折线图')) line.render('predict.html') 报错：FutureWarning: Unknown keyword arguments: dict_keys(['typ']).Passing unknown keyword arguments will raise a TypeError beginning in version 0.15. warnings.warn(msg, FutureWarning)

将以下两行代码：

predict_high = model_high.predict(start=len(high), end=len(high) + 364, typ='levels')
predict_low = model_low.predict(start=len(low), end=len(low) + 364, typ='levels')

修改为：

predict_high = model_high.predict(start=len(high), end=len(high) + 364)
predict_low = model_low.predict(start=len(low), end=len(low) + 364)

即可正常运行，因为ARIMA模型的predict方法不再接受typ参数，所以需要将其删除。

相关问题

修改以下代码，使程序能正常运行： import pandas as pdfrom statsmodels.tsa.arima.model import ARIMAfrom pyecharts import options as optsfrom pyecharts.charts import Lineweather1 = pd.read_csv('weather.csv', encoding='gb18030')weather2 = pd.read_csv('weather2.csv', encoding='gb18030')weather = pd.concat([weather1, weather2], ignore_index=True)# 将日期作为索引weather.set_index('日期', inplace=True)# 将气温转换为时间序列ts_high = pd.Series(weather['最高气温'].values, index=weather.index)ts_low = pd.Series(weather['最低气温'].values, index=weather.index)# 拟合ARIMA模型model_high = ARIMA(ts_high, order=(3, 1, 1)).fit()model_low = ARIMA(ts_low, order=(3, 1, 1)).fit()# 预测2023年的气温pred_high = model_high.predict('2023-01-01', '2023-12-31', dynamic=True)pred_low = model_low.predict('2023-01-01', '2023-12-31', dynamic=True)# 将预测结果合并到原始数据中weather_pred = pd.DataFrame({'最高气温': pred_high, '最低气温': pred_low}, index=pred_high.index)weather = pd.concat([weather, weather_pred], axis=0)line = Line()line.add_xaxis(weather.index)line.add_yaxis('最高气温', weather['最高气温'])line.add_yaxis('最低气温', weather['最低气温'])line.set_global_opts( title_opts=opts.TitleOpts(title='2023年气温预测'), xaxis_opts=opts.AxisOpts(name='日期'), yaxis_opts=opts.AxisOpts(name='气温（℃）'), legend_opts=opts.LegendOpts(pos_right='10%'))line.render('weathers5.html')print('2023年最高气温预测：')print(weather['最高气温'].loc['2023-01-01':'2023-12-31'])print('2023年最低气温预测：')print(weather['最低气温'].loc['2023-01-01':'2023-12-31']) 报的错误：ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.

可以在设置时间序列时，指定时间频率。修改代码如下：

import pandas as pd
from statsmodels.tsa.arima.model import ARIMA
from pyecharts import options as opts
from pyecharts.charts import Line

weather1 = pd.read_csv('weather.csv', encoding='gb18030')
weather2 = pd.read_csv('weather2.csv', encoding='gb18030')
weather = pd.concat([weather1, weather2], ignore_index=True)

# 将日期作为索引
weather.set_index('日期', inplace=True)

# 将气温转换为时间序列，并指定时间频率为日
ts_high = pd.Series(weather['最高气温'].values, index=pd.date_range(start='2021-01-01', periods=len(weather), freq='D'))
ts_low = pd.Series(weather['最低气温'].values, index=pd.date_range(start='2021-01-01', periods=len(weather), freq='D'))

# 拟合ARIMA模型
model_high = ARIMA(ts_high, order=(3, 1, 1)).fit()
model_low = ARIMA(ts_low, order=(3, 1, 1)).fit()

# 预测2023年的气温
pred_high = model_high.predict(start='2023-01-01', end='2023-12-31', dynamic=True, typ='levels')
pred_low = model_low.predict(start='2023-01-01', end='2023-12-31', dynamic=True, typ='levels')

# 将预测结果合并到原始数据中
weather_pred = pd.DataFrame({'最高气温': pred_high, '最低气温': pred_low}, index=pd.date_range(start='2023-01-01', end='2023-12-31', freq='D'))
weather = pd.concat([weather, weather_pred], axis=0)

line = Line()
line.add_xaxis(weather.index)
line.add_yaxis('最高气温', weather['最高气温'])
line.add_yaxis('最低气温', weather['最低气温'])
line.set_global_opts(
    title_opts=opts.TitleOpts(title='2023年气温预测'),
    xaxis_opts=opts.AxisOpts(name='日期'),
    yaxis_opts=opts.AxisOpts(name='气温（℃）'),
    legend_opts=opts.LegendOpts(pos_right='10%')
)
line.render('weathers5.html')

print('2023年最高气温预测：')
print(weather['最高气温'].loc['2023-01-01':'2023-12-31'])
print('2023年最低气温预测：')
print(weather['最低气温'].loc['2023-01-01':'2023-12-31'])

这样时间序列就会被正确的处理，并且能够进行预测。

请修改以下代码： import pandas as pdimport numpy as np # 新增代码import statsmodels.api as smfrom pyecharts.charts import Lineimport pyecharts.options as opts# 将weather.csv文件的内容追加到weather2.csv中with open('weather2.csv', 'ab')as f: f.write(open('weather.csv', 'rb').read())# 读取数据df = pd.read_csv('weather2.csv', encoding='gb18030', dtype={'最高气温': np.float64}) # 修改代码# 建立ARIMA模型model = sm.tsa.ARIMA(df['最高气温'], order=(2, 1, 1))result = model.fit()# 预测下一年的天气情况forecast = result.forecast(steps=365)# 绘制预测结果的折线图line = Line()line.add_xaxis(pd.date_range(start='2023-01-01', periods=365))line.add_yaxis('预测最高气温', forecast, is_smooth=True)line.set_global_opts( title_opts=opts.TitleOpts(title='2022年西安每月最高气温与最低气温平均数'), legend_opts=opts.LegendOpts(orient='vertical', pos_top='15%', pos_left='2%'),)line.render('weathers5.html') 错误有： TypeError: Cannot cast array data from dtype('O') to dtype('float64') according to the rule 'safe' ValueError: could not convert string to float: '最高气温'

请修改代码如下：

import pandas as pd
import numpy as np
import statsmodels.api as sm
from pyecharts.charts import Line
import pyecharts.options as opts

# 将weather.csv文件的内容追加到weather2.csv中
with open('weather2.csv', 'ab') as f:
    f.write(open('weather.csv', 'rb').read())

# 读取数据
df = pd.read_csv('weather2.csv', encoding='gb18030', dtype={'最高气温': np.float64}, skiprows=1)

# 建立ARIMA模型
model = sm.tsa.ARIMA(df['最高气温'], order=(2, 1, 1))
result = model.fit()

# 预测下一年的天气情况
forecast = result.forecast(steps=365)[0]

# 绘制预测结果的折线图
line = Line()
line.add_xaxis(pd.date_range(start='2023-01-01', periods=365))
line.add_yaxis('预测最高气温', forecast, is_smooth=True)
line.set_global_opts(
    title_opts=opts.TitleOpts(title='2022年西安每月最高气温与最低气温平均数'),
    legend_opts=opts.LegendOpts(orient='vertical', pos_top='15%', pos_left='2%'),
)
line.render('weathers5.html')

主要修改了以下三点：

1. 新增了 `skiprows=1`，跳过第一行的表头。
2. 修改了 `forecast` 的取值，由 `result.forecast(steps=365)` 改成了 `result.forecast(steps=365)[0]`，因为 `result.forecast()` 返回的是一个元组，第一个元素是预测结果，第二个元素是预测结果的标准误差。
3. 修改了 `df` 的读取方式，由 `pd.read_csv('weather2.csv', encoding='gb18030', dtype={'最高气温': np.float64})` 改成了 `pd.read_csv('weather2.csv', encoding='gb18030', dtype={'最高气温': np.float64}, skiprows=1)`，因为第一行是表头，不是数据。