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docs/source/user_guide/operators/forecast_operator/index.rst

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pip install "oracle_ads[forecast]"
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.. rst-class:: page-break
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🚀 Getting Started
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==================
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ads operator run -f forecast.yaml
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Using the API
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---------------
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result = operate(config)
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Using the Notebook UI
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------------------------
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.. image:: ./images/notebook_form_filled.png
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🧠 Tweak the Model
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===================
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name: arima
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The model name can be any of the following:
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- **Prophet** - Recommended for smaller datasets, and datasets with seasonality or holidays
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- **ARIMA** - Recommended for highly cyclical datasets
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- **AutoMLx** - Oracle Lab's proprietary modelling framework
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- **NeuralProphet** - Recommended for large or wide datasets
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- **AutoTS** - M6 Benchmark winner. Recommended if the other frameworks aren't providing enough accuracy
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- **Auto-Select** - The best of all of the above. Recommended for comparing the above frameworks. Caution, it can be very slow.
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- **Prophet** - Recommended for smaller datasets, and datasets with seasonality or holidays
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- **ARIMA** - Recommended for highly cyclical datasets
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- **AutoMLx** - Oracle Lab's proprietary modelling framework
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- **NeuralProphet** - Recommended for large or wide datasets
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- **AutoTS** - M6 Benchmark winner. Recommended if the other frameworks aren't providing enough accuracy
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- **Auto-Select** - The best of all of the above. Recommended for comparing the above frameworks. Caution, it can be very slow.
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Auto-Select the Best Model
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seasonality_mode: multiplicative
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changepoint_prior_scale: 0.05
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➕ Add Additional Column(s)
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Notice that this additional_data would only be capable of forecasting a horizon of 1 (on 01-03-2024).
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Sourcing Data
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=================
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target_column: y
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generate_explanations: True
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🧾 Disable File Generation
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generate_explanations_file: False
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generate_metrics_file: False
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📏 Change Evaluation Metric
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target_column: y
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metric: rmse
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🧵 Run as a Job
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============================

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