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DOC: Tutorial on cross-validation #1176

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merged 9 commits into from
Oct 22, 2024
804 changes: 804 additions & 0 deletions nbs/docs/capabilities/08_cross_validation.ipynb

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"\n",
"Follow this article for a step to step guide on building a production-ready forecasting pipeline for multiple time series. \n",
"\n",
"During this guide you will gain familiary with the core `NueralForecast`class and some relevant methods like `NeuralForecast.fit`, `NeuralForecast.predict`, and `StatsForecast.cross_validation.`\n",
"During this guide you will gain familiarity with the core `NeuralForecast`class and some relevant methods like `NeuralForecast.fit`, `NeuralForecast.predict`, and `StatsForecast.cross_validation.`\n",
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"\n",
"We will use a classical benchmarking dataset from the M4 competition. The dataset includes time series from different domains like finance, economy and sales. In this example, we will use a subset of the Hourly dataset. \n",
"\n",
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"docs/capabilities/overview.html",
"docs/capabilities/objectives.html",
"docs/capabilities/exogenous_variables.html",
"docs/capabilities/cross_validation.html",
"docs/capabilities/hyperparameter_tuning.html",
"docs/capabilities/predictinsample.html",
"docs/capabilities/save_load_models.html",
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"models.nhits.html",
"models.nlinear.html",
"models.patchtst.html",
"models.rmok.html",
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"models.rnn.html",
"models.softs.html",
"models.stemgnn.html",
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- models.nhits.ipynb
- models.nlinear.ipynb
- models.patchtst.ipynb
- models.rmok.ipynb
- models.rnn.ipynb
- models.softs.ipynb
- models.stemgnn.ipynb
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