forecast_recurrent error

[RageAgainstTheMachine101]

Hello!
I like your lib! But I've got some errors:

AttributeError Traceback (most recent call last)
~/Documents/notebooks/vk/SSA/mySSA.py in forecast_recurrent(self, steps_ahead, singular_values, plot, return_df, **plotargs)
206 try:
--> 207 self.X_com_hat
208 except(AttributeError):

AttributeError: 'mySSA' object has no attribute 'X_com_hat'

During handling of the above exception, another exception occurred:

KeyError Traceback (most recent call last)
~/Documents/notebooks/vk/SSA/mySSA.py in _forecast_prep(self, singular_values)
185 for i in singular_values:
--> 186 self.forecast_orthonormal_base[i] = self.orthonormal_base[i]
187 except:

KeyError: 3

During handling of the above exception, another exception occurred:

TypeError Traceback (most recent call last)
in ()
----> 1 ssa.forecast_recurrent(steps_ahead=ssa.ts.shape[0], singular_values=streams10, plot=True)

~/Documents/notebooks/vk/SSA/mySSA.py in forecast_recurrent(self, steps_ahead, singular_values, plot, return_df, **plotargs)
207 self.X_com_hat
208 except(AttributeError):
--> 209 self._forecast_prep(singular_values)
210 self.ts_forecast = np.array(self.ts_v[0])
211 for i in range(1, self.ts_N+steps_ahead):

~/Documents/notebooks/vk/SSA/mySSA.py in _forecast_prep(self, singular_values)
189 self.forecast_orthonormal_base[0] = self.orthonormal_base[0]
190 else:
--> 191 raise('Please pass in a list/array of singular value indices to use for forecast')
192 else:
193 self.forecast_orthonormal_base = self.orthonormal_base

TypeError: exceptions must derive from BaseException

[arvinnick]

Hi
I had your issue. I found that the initial list for singular values parameter is "stream10" which is 0 to 10. But it should be a list consisting of your produced signals (the X axis of the first bar chart).
I have some other questions as well. Why this list only has zero for me?!:)

[vic1309]

Hey! Did you manage to solve this issue?

The issue is related to the number of contributions. In the example, there are 15 arrays while, at least in my dataset, only 1 component is present. However, I'm not sure why this happens. Is this related to how 'smooth' is your dataset?