Description
Pandas version checks
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I have checked that this issue has not already been reported.
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I have confirmed this bug exists on the latest version of pandas.
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I have confirmed this bug exists on the main branch of pandas.
Reproducible Example
import pandas as pd
import numpy as np
np.random.seed(42)
df = pd.Series(np.random.normal(size=1200))
df_std_stock = (
df.rolling(window=220, min_periods=100, win_type='exponential')
.std(tau=-(25 / np.log(2)), center=0, sym=False)
)Issue Description
Hey, Team! The series of standard deviations produced by the rolling calculation with custom weights does not look right and I hope that a visual demonstration illustrates that best.
The blue line is the result of the calculation above and it is visible how after window observations the series no longer produces sensible results. Odd and strong changes in behavior are visible every pass over a window multiple, which is even more suspicious. The orange series is what I would expect the result to be and the calculation snippet that produced it is in the box below. The algorithm for the expected behavior is known to be problematic though.
Thank you for taking the time to consider my report and please let me know if further details are required. Lover your package and really hope that his helps making it even better.
Expected Behavior
import pandas as pd
import numpy as np
np.random.seed(42)
df = pd.Series(np.random.normal(size=1200))
df_std_custom = (
(df ** 2).rolling(window=220, min_periods=100, win_type='exponential')
.mean(tau=-(25 / np.log(2)), center=0, sym=False)
)
df_std_custom_mean = (
df.rolling(window=220, min_periods=100, win_type='exponential')
.mean(tau=-(25 / np.log(2)), center=0, sym=False)
)
df_std_custom = (df_std_custom - df_std_custom_mean**2).pow(0.5)Installed Versions
INSTALLED VERSIONS
commit : 37ea63d
python : 3.9.16.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19044
machine : AMD64
processor : Intel64 Family 6 Model 141 Stepping 1, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : English_United States.1252
pandas : 2.0.1
numpy : 1.22.4
pytz : 2023.3
dateutil : 2.8.2
setuptools : 66.0.0
pip : 23.0.1
Cython : 0.29.34
pytest : 7.3.1
hypothesis : 6.75.3
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.1.2
IPython : 8.13.2
pandas_datareader: None
bs4 : 4.12.2
bottleneck : None
brotli : None
fastparquet : None
fsspec : None
gcsfs : None
matplotlib : 3.7.1
numba : None
numexpr : 2.8.4
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 12.0.0
pyreadstat : None
pyxlsb : None
s3fs : None
scipy : 1.7.1
snappy : None
sqlalchemy : None
tables : 3.8.0
tabulate : None
xarray : None
xlrd : None
zstandard : None
tzdata : 2023.3
qtpy : None
pyqt5 : None