时间序列分析-间隔不均匀的度量-熊猫+统计模型

Question

我有两个numpy数组light_points和time_points，想对这些数据使用一些时间序列分析方法。

然后我尝试了这个：

import statsmodels.api as sm
import pandas as pd
tdf = pd.DataFrame({'time':time_points[:]})
rdf =  pd.DataFrame({'light':light_points[:]})
rdf.index = pd.DatetimeIndex(freq='w',start=0,periods=len(rdf.light))
#rdf.index = pd.DatetimeIndex(tdf['time'])

这有效，但没有做正确的事。确实，测量值不是均匀地间隔开的，如果我只是将time_points pandas DataFrame声明为帧的索引，则会出现错误：

rdf.index = pd.DatetimeIndex(tdf['time'])

decomp = sm.tsa.seasonal_decompose(rdf)

elif freq is None:
raise ValueError("You must specify a freq or x must be a pandas object with a timeseries index")

ValueError: You must specify a freq or x must be a pandas object with a timeseries index

我不知道该如何纠正。另外，似乎TimeSeries不建议使用大熊猫。

我尝试了这个：

rdf = pd.Series({'light':light_points[:]})
rdf.index = pd.DatetimeIndex(tdf['time'])

但这给了我长度上的不匹配：

ValueError: Length mismatch: Expected axis has 1 elements, new values have 122 elements

但是，我不明白它的来源，因为rdf [‘light’]和tdf [‘time’]的长度相同…

最终，我尝试将rdf定义为pandas系列：

rdf = pd.Series(light_points[:],index=pd.DatetimeIndex(time_points[:]))

我得到这个：

ValueError: You must specify a freq or x must be a pandas object with a timeseries index

然后，我尝试改为用

 pd.TimeSeries(time_points[:])

它给我在season_decompose方法行上的错误：

AttributeError: 'Float64Index' object has no attribute 'inferred_freq'

如何处理空间不均匀的数据？我当时正在考虑通过在现有值之间添加许多未知值并使用插值法“评估”这些点来创建一个近似均匀间隔的时间数组，但是我认为可以找到一种更干净，更轻松的解决方案。

Answer 1

seasonal_decompose()要求freq是作为DateTimeIndex元信息的一部分提供的，可以pandas.Index.inferred_freq由用户推断，也可以由用户推断为，integer它给出每个周期的周期数。例如，每月12次（从docstring到seasonal_mean）：

def seasonal_decompose(x, model="additive", filt=None, freq=None):
    """
    Parameters
    ----------
    x : array-like
        Time series
    model : str {"additive", "multiplicative"}
        Type of seasonal component. Abbreviations are accepted.
    filt : array-like
        The filter coefficients for filtering out the seasonal

component.
The default is a symmetric moving average.
freq : int, optional
Frequency of the series. Must be used if x is not a pandas
object with a timeseries index.

为了说明-使用随机样本数据：

    length = 400
    x = np.sin(np.arange(length)) * 10 + np.random.randn(length)
    df = pd.DataFrame(data=x, index=pd.date_range(start=datetime(2015, 1, 1), periods=length, freq='w'), columns=['value'])

    <class 'pandas.core.frame.DataFrame'>
    DatetimeIndex: 400 entries, 2015-01-04 to 2022-08-28
    Freq: W-SUN

    decomp = sm.tsa.seasonal_decompose(df)
    data = pd.concat([df, decomp.trend, decomp.seasonal, decomp.resid], axis=1)
    data.columns = ['series', 'trend', 'seasonal', 'resid']

    Data columns (total 4 columns):
    series      400 non-null float64
    trend       348 non-null float64
    seasonal    400 non-null float64
    resid       348 non-null float64
    dtypes: float64(4)
    memory usage: 15.6 KB

到目前为止，一切都很好-现在从中随机删除元素DateTimeIndex以创建空间不均匀的数据：

    df = df.iloc[np.unique(np.random.randint(low=0, high=length, size=length * .8))]

    <class 'pandas.core.frame.DataFrame'>
    DatetimeIndex: 222 entries, 2015-01-11 to 2022-08-21
    Data columns (total 1 columns):
    value    222 non-null float64
    dtypes: float64(1)
    memory usage: 3.5 KB

    df.index.freq

    None

    df.index.inferred_freq

    None

seasonal_decomp在此数据上运行“有效”：

```
decomp = sm.tsa.seasonal_decompose(df, freq=52)

data = pd.concat([df, decomp.trend, decomp.seasonal, decomp.resid], axis=1)
data.columns = ['series', 'trend', 'seasonal', 'resid']

DatetimeIndex: 224 entries, 2015-01-04 to 2022-08-07
Data columns (total 4 columns):
series      224 non-null float64
trend       172 non-null float64
seasonal    224 non-null float64
resid       172 non-null float64
dtypes: float64(4)
memory usage: 8.8 KB

```

问题是-
结果有多有用。即使数据之间没有缺口，也无法使季节模式的推断复杂化（请参阅发行说明.interpolate()中的示例使用，也可以使此过程符合以下条件：statsmodels

Notes
-----
This is a naive decomposition. More sophisticated methods should
be preferred.

The additive model is Y[t] = T[t] + S[t] + e[t]

The multiplicative model is Y[t] = T[t] * S[t] * e[t]

The seasonal component is first removed by applying a convolution
filter to the data. The average of this smoothed series for each
period is the returned seasonal component.