adjust number of lags about pyfts HOT 4 CLOSED

The number of lags can be chosen when you employ an high order method, like HOFTS (the standard), IFTS or PWFTS. In that cases you can inform the order (the number of lags) in the constructor using the 'order' parameter and the lag indexes using the 'lags' parameter. The default 'order' is 2 and the default 'lags' is [1,2]. Check the below the model creation for third order and lags 7,14,21:

from pyFTS.data import SONDA
from pyFTS.partitioners import Grid
from pyFTS.models import hofts

train = SONDA.get_data('glo_avg')[:800]
test = SONDA.get_data('glo_avg')[800:1000]

partitioner = Grid.GridPartitioner(data=train, npart=35)

model = hofts.HighOrderFTS(partitioner=partitioner, order=3, lags=[7, 14, 21])

model.fit(train)

print(model)

forecasts = model.predict(test)

from pyfts.

Thank you for the reply. Based on my understanding if the default order is 2 then I must specify a list with two values of lag. And on the example above the order is 3 that is why the lag has a value of three lags. If the order is 4 or more then lag has four or more values too?
Second question is how do we identify or determine the number of lags per order ( I assume ACF/PACF, but how?)?

Regards,

from pyfts.

Hi! You don't need to specify the lags, it is automatically generated as an incremental list. It is the default and it works for the majority of the situations. BUT sometimes there are more significant lags that help to identify complex patterns. In that case you must use the 'lags' parameter. ACF is a good start, but it won't work always, specially for long memory time series. I am currently working on a hyperparameter optimization tool that helps to identify the best lags.

from pyfts.

Great! My focus at the moment is long time series, I hope the tool will be available soon.

Regards,

from pyfts.