Cross-correlation (time-lag-correlation) with pandas?
Solution 1
As far as I can tell, there isn't a built in method that does exactly what you are asking. But if you look at the source code for the pandas Series method autocorr
, you can see you've got the right idea:
def autocorr(self, lag=1):
"""
Lag-N autocorrelation
Parameters
----------
lag : int, default 1
Number of lags to apply before performing autocorrelation.
Returns
-------
autocorr : float
"""
return self.corr(self.shift(lag))
So a simple timelagged cross covariance function would be
def crosscorr(datax, datay, lag=0):
""" Lag-N cross correlation.
Parameters
----------
lag : int, default 0
datax, datay : pandas.Series objects of equal length
Returns
----------
crosscorr : float
"""
return datax.corr(datay.shift(lag))
Then if you wanted to look at the cross correlations at each month, you could do
xcov_monthly = [crosscorr(datax, datay, lag=i) for i in range(12)]
Solution 2
There is a better approach: You can create a function that shifted your dataframe first before calling the corr().
Get this dataframe like an example:
d = {'prcp': [0.1,0.2,0.3,0.0], 'stp': [0.0,0.1,0.2,0.3]}
df = pd.DataFrame(data=d)
>>> df
prcp stp
0 0.1 0.0
1 0.2 0.1
2 0.3 0.2
3 0.0 0.3
Your function to shift others columns (except the target):
def df_shifted(df, target=None, lag=0):
if not lag and not target:
return df
new = {}
for c in df.columns:
if c == target:
new[c] = df[target]
else:
new[c] = df[c].shift(periods=lag)
return pd.DataFrame(data=new)
Supposing that your target is comparing the prcp (precipitation variable) with stp(atmospheric pressure)
If you do at the present will be:
>>> df.corr()
prcp stp
prcp 1.0 -0.2
stp -0.2 1.0
But if you shifted 1(one) period all other columns and keep the target (prcp):
df_new = df_shifted(df, 'prcp', lag=-1)
>>> print df_new
prcp stp
0 0.1 0.1
1 0.2 0.2
2 0.3 0.3
3 0.0 NaN
Note that now the column stp is shift one up position at period, so if you call the corr(), will be:
>>> df_new.corr()
prcp stp
prcp 1.0 1.0
stp 1.0 1.0
So, you can do with lag -1, -2, -n!!
Solution 3
To build up on Andre's answer - if you only care about (lagged) correlation to the target, but want to test various lags (e.g. to see which lag gives the highest correlations), you can do something like this:
lagged_correlation = pd.DataFrame.from_dict(
{x: [df[target].corr(df[x].shift(-t)) for t in range(max_lag)] for x in df.columns})
This way, each row corresponds to a different lag value, and each column corresponds to a different variable (one of them is the target itself, giving the autocorrelation).
JC_CL
Updated on July 23, 2022Comments
-
JC_CL almost 2 years
I have various time series, that I want to correlate - or rather, cross-correlate - with each other, to find out at which time lag the correlation factor is the greatest.
I found various questions and answers/links discussing how to do it with numpy, but those would mean that I have to turn my dataframes into numpy arrays. And since my time series often cover different periods, I am afraid that I will run into chaos.
Edit
The issue I am having with all the numpy/scipy methods, is that they seem to lack awareness of the timeseries nature of my data. When I correlate a time series that starts in say 1940 with one that starts in 1970, pandas
corr
knows this, whereasnp.correlate
just produces a 1020 entries (length of the longer series) array full of nan.The various Q's on this subject indicate that there should be a way to solve the different length issue, but so far, I have seen no indication on how to use it for specific time periods. I just need to shift by 12 months in increments of 1, for seeing the time of maximum correlation within one year.
Edit2
Some minimal sample data:
import pandas as pd import numpy as np dfdates1 = pd.date_range('01/01/1980', '01/01/2000', freq = 'MS') dfdata1 = (np.random.random_integers(-30,30,(len(dfdates1)))/10.0) #My real data is from measurements, but random between -3 and 3 is fitting df1 = pd.DataFrame(dfdata1, index = dfdates1) dfdates2 = pd.date_range('03/01/1990', '02/01/2013', freq = 'MS') dfdata2 = (np.random.random_integers(-30,30,(len(dfdates2)))/10.0) df2 = pd.DataFrame(dfdata2, index = dfdates2)
Due to various processing steps, those dfs end up changed into df that are indexed from 1940 to 2015. this should reproduce this:
bigdates = pd.date_range('01/01/1940', '01/01/2015', freq = 'MS') big1 = pd.DataFrame(index = bigdates) big2 = pd.DataFrame(index = bigdates) big1 = pd.concat([big1, df1],axis = 1) big2 = pd.concat([big2, df2],axis = 1)
This is what I get when I correlate with pandas and shift one dataset:
In [451]: corr_coeff_0 = big1[0].corr(big2[0]) In [452]: corr_coeff_0 Out[452]: 0.030543266378853299 In [453]: big2_shift = big2.shift(1) In [454]: corr_coeff_1 = big1[0].corr(big2_shift[0]) In [455]: corr_coeff_1 Out[455]: 0.020788314779320523
And trying scipy:
In [456]: scicorr = scipy.signal.correlate(big1,big2,mode="full") In [457]: scicorr Out[457]: array([[ nan], [ nan], [ nan], ..., [ nan], [ nan], [ nan]])
which according to
whos
isscicorr ndarray 1801x1: 1801 elems, type `float64`, 14408 bytes
But I'd just like to have 12 entries. /Edit2
The idea I have come up with, is to implement a time-lag-correlation myself, like so:
corr_coeff_0 = df1['Data'].corr(df2['Data']) df1_1month = df1.shift(1) corr_coeff_1 = df1_1month['Data'].corr(df2['Data']) df1_6month = df1.shift(6) corr_coeff_6 = df1_6month['Data'].corr(df2['Data']) ...and so on
But this is probably slow, and I am probably trying to reinvent the wheel here. Edit The above approach seems to work, and I have put it into a loop, to go through all 12 months of a year, but I still would prefer a built in method.