将ARMA模型拟合到python中按时间索引的时间序列
问题描述
我正在尝试将 ARMA 模型拟合到存储在 pandas 数据框中的时间序列.数据框有一列名为val"的 numpy.float64 类型的值和一个 pandas 时间戳索引.时间戳采用年-月-日时:分:秒"格式.我理解以下代码:
I am trying to fit an ARMA model to a time series stored in a pandas dataframe. The dataframe has one column of values of type numpy.float64 named "val" and an index of pandas timestamps. The timestamps are in the "Year-Month-Day Hour:Minute:Second" format. I understand that the following code:
from statsmodels.tsa.arima_model import ARMA
model = ARMA(df["val"], (1,0))
给我错误信息:
ValueError: Given a pandas object and the index does not contain dates
因为我没有正确格式化时间戳.如何索引我的数据帧,以便 ARMA 方法接受它,同时保留我的日期和时间信息?
because I have not formatted the timestamps correctly. How can I index my dataframe so that the ARMA method accepts it while retaining my date and time information?
解决方案
我认为你需要将 index
转换为 DatetimeIndex
:
I think you need convert index
to DatetimeIndex
:
df.index = pd.DatetimeIndex(df.index)
示例:
import pandas as pd
from statsmodels.tsa.arima_model import ARMA
df=pd.DataFrame({"val": pd.Series([1.1,1.7,8.4 ],
index=['2015-01-15 12:10:23','2015-02-15 12:10:23','2015-03-15 12:10:23'])})
print df
val
2015-01-15 12:10:23 1.1
2015-02-15 12:10:23 1.7
2015-03-15 12:10:23 8.4
print df.index
Index([u'2015-01-15 12:10:23',u'2015-02-15 12:10:23',u'2015-03-15 12:10:23'], dtype='object')
df.index = pd.DatetimeIndex(df.index)
print df.index
DatetimeIndex(['2015-01-15 12:10:23', '2015-02-15 12:10:23',
'2015-03-15 12:10:23'],
dtype='datetime64[ns]', freq=None)
model = ARMA(df["val"], (1,0))
print model
<statsmodels.tsa.arima_model.ARMA object at 0x000000000D5247B8>
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