CODEWITHSUNDEEP
pythonstatsmodels
user466534
user466534

Reputation:

freq parameter not presented in seasonal_decompose function

let us suppose we have following code :

import yfinance as yf
import matplotlib.pyplot as plt
data =  yf.download(tickers="FB", start="2016-1-1",end="2022-05-15",progress=False)
data.drop(["Open","Low","Close","Adj Close","Volume"],axis=1,inplace=True)
data.sort_index(inplace=True)
print(data.head())
from statsmodels.tsa.seasonal import  seasonal_decompose
result =seasonal_decompose(data["High"],model='multiplicative',freq=36)
estimated_trend_add = result.trend
estimated_seasonal_add = result.seasonal
estimated_residual_add = result.resid
fig, axes = plt.subplots(4, 1)
fig.set_figheight(10)
fig.set_figwidth(15)
axes[0].plot(data['High'], label='Original')
axes[0].legend(loc='upper left')
axes[1].plot(estimated_trend_add, label='Trend')
axes[1].legend(loc='upper left')
axes[2].plot(estimated_seasonal_add, label='Cyclic')
axes[2].legend(loc='upper left')
axes[3].plot(estimated_residual_add, label='Residuals')
axes[3].legend(loc='upper left')
plt.show()

result of dataframe is given below :

                 High
Date                  
2015-12-31  106.169998
2016-01-04  102.239998
2016-01-05  103.709999
2016-01-06  103.769997
2016-01-07  101.430000

also it returns following error :

Traceback (most recent call last):
  File "C:\Users\User\PycharmProjects\DataScience\facebook_real_prices.py", line 8, in <module>
    result =seasonal_decompose(data["High"],model='multiplicative',freq=36)
TypeError: seasonal_decompose() got an unexpected keyword argument 'freq'

instead i know that there is keyword period, are they same?generally we know that period is 1/frequency, but in this case how should i define period? or how can i create frequency index? please help me

Upvotes: 0

Views: 1603

Answers (1)

Tim
Tim

Reputation: 248

The period argument in seasonal_decompose is periodicity i.e after how long things start to repeat.

For example, time series data with a frequency of 1 hour would have a period of 24. If frequency was 3 hours, period would be 8 = (24 / 3).

In your case, it seems that the frequency is business days (Mon - Fri), which implies a period of 5.

Yeah, it seems that both weekends and holidays have been removed. But the asfreq method can be used to forwad-fill/back-fill blanks. Specifying a period of 5 means that you expect values e.g on Mondays to be somewhat similar

If the input data is a pandas.Series with a datetime index, you might not need to specify a period:

data = data.asfreq(pd.offsets.BDay(), method="pad")

But be warned, the statsmodels.tsa.tsatools.freq_to_period function used internally in seasonal_decompose ignores the scale of frequency i.e. 3H and H are both treated as H. You can check this with:

from statsmodels.tsa.tsatools import freq_to_period
assert freq_to_period("H") == freq_to_period("3H") == 24

I would recommend specifying the period argument directly in seasonal_decompose (5 in this case).

Upvotes: 1

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