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ValueError: Length of endogenous variable must be larger the the number of lags used in the model and the number of observations burned in the log-likelihood calculation #42
I tried to run Time Series Forecastings.ipynb both in Jupiter and python script. From Jupiter it seems fine. If I tried to run as a python file (paste sections one by one and run as whole), in
Traceback (most recent call last):
File "time-series.py", line 71, in <module>
results.plot_diagnostics(figsize=(16, 8))
File "/home/user/anaconda3/lib/python3.8/site-packages/statsmodels/tsa/statespace/mlemodel.py", line 4284, in plot_diagnostics
raise ValueError(
ValueError: Length of endogenous variable must be larger the the number of lags used in the model and the number of observations burned in the log-likelihood calculation.
Ensure that you have enough data points for the chosen model. The number of lags used in the model and the number of burn-in observations should be less than the length of your time series.
If you're specifying the number of lags or other parameters for your time series model, try reducing them to a level that is appropriate for your data. If you're testing your model on a small subsample of your data, consider using a larger portion of your data for testing. The problem you're facing can happen if you don't have enough data points for the chosen model.
ValueError: Length of endogenous variable must be larger the the number of lags used in the model and the number of observations burned in the log-likelihood calculation · Issue #42 · susanli2016/Machine-Learning-with-Python
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aniketDash7 commentedon Oct 5, 2023
Ensure that you have enough data points for the chosen model. The number of lags used in the model and the number of burn-in observations should be less than the length of your time series.
If you're specifying the number of lags or other parameters for your time series model, try reducing them to a level that is appropriate for your data. If you're testing your model on a small subsample of your data, consider using a larger portion of your data for testing. The problem you're facing can happen if you don't have enough data points for the chosen model.