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I'm currently exploring a couple of statistical forecasting methods. My problem rose when using VARMA(2,1) fixed window rolling forecast. The example code that I'm using is the following: Here I only used 2 series out of 24.

import pandas as pd
from statsmodels.tsa.statespace.varmax import VARMAX

# Parameters
window = 90

# Prepare the two series and train/test split
series_df = dfp[['H0', 'H1']]
n_test     = int(len(series_df) * 0.1)
n_train    = len(series_df) - n_test

test_index = series_df.index[n_train:]
forecasts  = pd.DataFrame(index=test_index, columns=series_df.columns, dtype=float)

# Rolling one‐step‐ahead forecasts using VARMAX(2,1)
for i, dt in enumerate(test_index):
    end        = n_train + i
    start      = end - window
    history_df = series_df.iloc[start:end]

    model = VARMAX(
        history_df,
        order=(2, 1),             # AR=2, MA=1
        trend='c',
        enforce_stationarity=False,
        enforce_invertibility=False
    )
    res = model.fit(maxiter=10, disp=False)

    # forecast returns a DataFrame with one row and two columns
    fc = res.forecast(steps=1)
    forecasts.iloc[i] = fc.iloc[0]

The forecast window size is 90 days, and when applying it to the test set, we talk about 131 1-step-ahead forecasts. For my application it is necessary that I take the full 24-dimensional dataframe.

My question now is, is there a way to speed this up. I'm running it on the 2 series in a colab environment and it takes a minute or so. Using the entire 24 dimensional dataframe, after hours the forecast hasnt been concluded yet. Not only that I tried in Python but also in R too, and encountered the same issue. I also tried to give the VARMAX function a warmstart, by feeding the prior estimates into each new window, also without success. Another question that I have is, I've investigated the internal construction of the VARMAX function of statsmodels, and by default, any VARMA model is internally transferred into a linear state space model. Would it be possible to talk for example the tensorflow tsa libraries, that have a state space model available that runs on GPU and it would lead to the same (or very similar results) ?

Has anyone an idea?

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    $\begingroup$ How large is the time series dataframe? $\endgroup$ Commented May 30 at 0:12
  • $\begingroup$ @paisanco, it’s not that large considering I have only a 90day window and 131 (10% of the data) forecasts with re-estimation at each time step. But it is 24-dimensional. $\endgroup$ Commented May 31 at 10:16
  • $\begingroup$ The only VARMA code I could find for Tensorflow is on a Github several years old that may not work with current TensorFLow (have not tried it) github.com/OAID/TensorFlow-HRT/blob/master/tensorflow/contrib/… $\endgroup$ Commented May 31 at 15:55
  • $\begingroup$ @paisanco thank you, I found the same already and couldnt make it run :/ you have any idea why the code i posted takes so much time? Like do you see anything wrong or is there anything that I'm not thinking of ? $\endgroup$ Commented Jun 1 at 12:29

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