Decomposing trend, seasonal and residual time series elements

Question

I have a DataFrame with a few time series:

         divida    movav12       var  varmovav12
Date                                            
2004-01       0        NaN       NaN         NaN
2004-02       0        NaN       NaN         NaN
2004-03       0        NaN       NaN         NaN
2004-04      34        NaN       inf         NaN
2004-05      30        NaN -0.117647         NaN
2004-06      44        NaN  0.466667         NaN
2004-07      35        NaN -0.204545         NaN
2004-08      31        NaN -0.114286         NaN
2004-09      30        NaN -0.032258         NaN
2004-10      24        NaN -0.200000         NaN
2004-11      41        NaN  0.708333         NaN
2004-12      29  24.833333 -0.292683         NaN
2005-01      31  27.416667  0.068966    0.104027
2005-02      28  29.750000 -0.096774    0.085106
2005-03      27  32.000000 -0.035714    0.075630
2005-04      30  31.666667  0.111111   -0.010417
2005-05      31  31.750000  0.033333    0.002632
2005-06      39  31.333333  0.258065   -0.013123
2005-07      36  31.416667 -0.076923    0.002660

I want to decompose the first time series divida in a way that I can separate its trend from its seasonal and residual components.

I found an answer here, and am trying to use the following code:

import statsmodels.api as sm

s=sm.tsa.seasonal_decompose(divida.divida)

However I keep getting this error:

Traceback (most recent call last):
File "/Users/Pred_UnBR_Mod2.py", line 78, in <module> s=sm.tsa.seasonal_decompose(divida.divida)
File "/Library/Python/2.7/site-packages/statsmodels/tsa/seasonal.py", line 58, in seasonal_decompose _pandas_wrapper, pfreq = _maybe_get_pandas_wrapper_freq(x)
File "/Library/Python/2.7/site-packages/statsmodels/tsa/filters/_utils.py", line 46, in _maybe_get_pandas_wrapper_freq
freq = index.inferred_freq
AttributeError: 'Index' object has no attribute 'inferred_freq'

How can I proceed?

Answer 1

Works fine when you convert your index to DateTimeIndex:

df.reset_index(inplace=True)
df['Date'] = pd.to_datetime(df['Date'])
df = df.set_index('Date')
s=sm.tsa.seasonal_decompose(df.divida)

<statsmodels.tsa.seasonal.DecomposeResult object at 0x110ec3710>

Access the components via:

s.resid
s.seasonal
s.trend

Answer 2

Statsmodel will decompose the series only if you provide frequency. Usually all time series index will contain frequency eg: Daywise, Business days, weekly So it shows error. You can remove this error by two ways:

1. What Stefan did is he gave the index column to pandas DateTime function. It uses internal function infer_freq to find the frequency and return the index with frequency.
2. Else you can set the frequency to your index column as df.index.asfreq(freq='m'). Here m represents month. You can set the frequency if you have domain knowledge or by d.

Answer 3

It depends on the index format. You can have DateTimeIndex or you can have PeriodIndex. Stefan presented the example for DateTimeIndex. Here is my example for PeriodIndex. My original DataFrame has a MultiIndex index with year in first level and month in second level. Here is how I convert it to PeriodIndex:

df["date"] = pd.PeriodIndex (df.index.map(lambda x: "{0}{1:02d}".format(*x)),freq="M")
df = df.set_index("date")

Now it is ready to be used by seasonal_decompose.

Answer 4

Make it simple:

Follow three steps:
1. if not done, make the column in yyyy-mm-dd or dd-mm-yyyy( using excel).
2. Then using pandas convert it into date format as: df['Date'] = pd.to_datetime(df['Date'])
3. decompose it using:

from statsmodels.tsa.seasonal import seasonal_decompose
decomposition=seasonal_decompose(ts_log)

And finally:

Answer 5

Try parsing the date column using parse_dates , and later mention the index column .

from statsmodels.tsa.seasonal import seasonal_decompose
data=pd.read_csv(airline,header=0,squeeze=True,index_col=[0],parse_dates=[0])
res=seasonal_decompose(data)