I am using a publicly available data Kaggle: Rossmann Store Sales and trying to forecast sales. I am using Python.
My timeseries is stationary, confirmed via the Dickey-Fuller test. However, I wanted to perform seasonal decomposition.
I performed seasonal decompositions using statsmodels.tsa.seasonal.seasonal_decompose
. And my seasonal decomposition looks like this:
When I plot ACF of residuals
there appears to be too much autocorelation!
Am I doing something wrong? or looking at it the wrong way?
My understanding is residuals
should show no autocorelation because trend
and seasonal
have been taken out or adjusted for.
Update 1: Using freq=13
I perform seasonal decomposition and ACF of residuals is given below:
Update 2: As requested by @IrishStat, I am posting the original data
Head(10):
Date
2013-01-01 0
2013-01-02 5737
2013-01-03 5292
2013-01-04 5623
2013-01-05 5018
2013-01-06 0
2013-01-07 9277
2013-01-08 7479
2013-01-09 6681
2013-01-10 6680
Name: Sales, dtype: int64
This is the plot of original data:
freq
inseasonal_decompose
? The figures were initially generated by usingfreq=30
, I have updated withfreq=13
. $\endgroup$