How do I input a Time Series in spmvg nfoursid

Question

I want to use this algorithm for n4sid model estimation. However, in the Documentation, there is an input DataFrame generated from Random Samples, where I want to input a Time Series Dataframe. Calling the nfoursid method leads to an Type Error or Value Error.

Documentation:
https://github.com/spmvg/nfoursid/blob/master/examples/Overview.ipynb

Imported libs:

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from nfoursid.kalman import Kalman
from nfoursid.nfoursid import NFourSID
from nfoursid.state_space import StateSpace
import time
import datetime
import math
import scipy as sp

My input Time Series as Data Frame (flawless):

import yfinance as yfin
yfin.pdr_override()
spy = pdr.get_data_yahoo('AAPL',start='2022-08-23',end='2022-10-24')
spy['Log Return'] = np.log(spy['Adj Close']/spy['Adj Close'].shift(1))
AAPL=pd.DataFrame((spy['Log Return']))

The input DataFrame as proposed in the documentation:

state_space = StateSpace(A, B, C, D)
for _ in range(NUM_TRAINING_DATAPOINTS):
    input_state = np.random.standard_normal((INPUT_DIM, 1))
    noise = np.random.standard_normal((OUTPUT_DIM, 1)) * NOISE_AMPLITUDE
    state_space.step(input_state, noise)

The call using the input proposed in the documentation:

#---->libs already imported

pd.set_option('display.max_columns', None)
np.random.seed(0)  # reproducible results

NUM_TRAINING_DATAPOINTS = 1000  
# create a training-set by simulating a state-space model with this many datapoints
NUM_TEST_DATAPOINTS = 20  # same for the test-set
INPUT_DIM = 3 #---->this probably needs to adapted to the AAPL dimensions
OUTPUT_DIM = 2
INTERNAL_STATE_DIM = 4  # actual order of the state-space model in the training- and test-set
NOISE_AMPLITUDE = .1  # add noise to the training- and test-set
FIGSIZE = 8

# define system matrices for the state-space model of the training- and test-set
A = np.array([
    [1,  .01,    0,   0],
    [0,    1,  .01,   0],
    [0,    0,    1, .02],
    [0, -.01,    0,   1],
]) / 1.01
B = np.array([
    [1, 0, 0],
    [0, 1, 0],
    [0, 0, 1],
    [0, 1, 1],
]
) / 3
C = np.array([
    [1, 0, 1,  1],
    [0, 0, 1, -1],
])
D = np.array([
    [1, 0, 1],
    [0, 1, 0]
]) / 10
)



#---->maybe I have to input the DataFrame already here at the state-space model:

state_space = StateSpace(A, B, C, D)
for _ in range(NUM_TRAINING_DATAPOINTS):
    input_state = np.random.standard_normal((INPUT_DIM, 1))
    noise = np.random.standard_normal((OUTPUT_DIM, 1)) * NOISE_AMPLITUDE

    state_space.step(input_state, noise)

#----




#---->This is the method with the input DF, in this case the random state-space model

nfoursid = NFourSID(
    state_space.to_dataframe(),  # the state-space model can summarize inputs and outputs as a dataframe
    output_columns=state_space.y_column_names,
    input_columns=state_space.u_column_names,
    num_block_rows=10
)
nfoursid.subspace_identification()

Pasting my DF at the call of the method nfoursid which leads to an error:

df2 = pd.DataFrame()

nfoursid = NFourSID(
    output_columns=df2,
    input_columns=AAPL,
    num_block_rows=10
)

TypeError: NFourSID.init() missing 1 required positional argument: 'dataframe'

Pasting DF in the state_space led to:

ValueError: Dimensions of u (43, 1) are inconsistent. Expected (3, 1). and TypeError: 'DataFrame' object is not callable