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refresh notebooks docs and examples
- [x] remove outdated notebook - [x] remove duplicates from examples - [x] refresh wording with cmdstanpy instead of pystan
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‎docs/tutorials/build_your_own_model.ipynb

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"2024-01-11 22:10:27 - orbit - INFO - Using SVI (Pyro) with steps: 501, samples: 100, learning rate: 0.1, learning_rate_total_decay: 1.0 and particles: 100.\n",
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"2024-01-21 13:47:27 - orbit - INFO - Using SVI (Pyro) with steps: 501, samples: 100, learning rate: 0.1, learning_rate_total_decay: 1.0 and particles: 100.\n",
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"2024-01-21 13:47:27 - orbit - INFO - step 0 loss = 27333, scale = 0.077497\n",
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‎docs/tutorials/decompose_prediction.ipynb

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"2024-01-21 14:39:49 - orbit - INFO - Sampling (CmdStanPy) with chains: 4, cores: 8, temperature: 1.000, warmups (per chain): 225 and samples(per chain): 25.\n"
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‎docs/tutorials/dlt.ipynb

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"tags": []
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"source": [
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"### High Dimensional and Fourier Series Regression\n",
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"In case of high dimensional regression, users can consider fixing the smoothness with a relatively small levels smoothing values e.g. setting `level_sm_input=0.01`. This is particularly useful in modeling higher frequency time-series such as daily and hourly data using regression on Fourier series. Check out the `examples/` folder for more details."
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"In case of high dimensional regression, users can consider fixing the smoothness with a relatively small levels smoothing values e.g. setting `level_sm_input=0.01`. This is particularly useful in modeling higher frequency time-series such as daily and hourly data using regression on Fourier series. Check out the `examples/` folder for the details."
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‎docs/tutorials/ets_lgt_dlt_missing_response.ipynb

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"end_time": "2021-12-16T20:11:05.798766Z",

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