Merge pull request #108 from databricks-industry-solutions/switch-forecasting_conf

made forecasting_conf file dynamic based on the frequency

Author: ryuta-yoshimatsu

README.md

@@ -112,7 +112,7 @@ run_forecast(
 - ```experiment_path``` to keep metrics under the MLFlow.
 - ```use_case_name``` a new column will be created under the delta Table, in case you save multiple trials under 1 table.
 
-To modify the model hyperparameters, change the values in [mmf_sa/models/models_conf.yaml](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/models/models_conf.yaml) or overwrite these values in [mmf_sa/forecasting_conf.yaml](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/forecasting_conf.yaml). 
+To modify the model hyperparameters, change the values in [mmf_sa/models/models_conf.yaml](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/models/models_conf.yaml) or overwrite these values, for example, in [mmf_sa/forecasting_conf_daily.yaml](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/forecasting_conf_daily.yaml) if your frequency is `D`. 
 
 MMF is fully integrated with MLflow, so once the training kicks off, the experiments will be visible in the MLflow Tracking UI with the corresponding metrics and parameters (note that we do not log all local models in MLFlow, but we store the binaries in the tables ```evaluation_output``` and ```scoring_output```). The metric you see in the MLflow Tracking UI is a simple mean over backtesting trials over all time series. Refer to the [notebook](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/post-evaluation-analysis.ipynb) for guidance on performing fine-grained model selection after running `run_forecast`.
 
@@ -189,7 +189,7 @@ The parameters are all the same except:
 -  ```use_case_name``` will be used to suffix the model name when registered to Unity Catalog.
 -  ```accelerator``` tells MMF to use GPU instead of CPU.
 
-To modify the model hyperparameters or reset the range of the hyperparameter search, change the values in [mmf_sa/models/models_conf.yaml](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/models/models_conf.yaml) or overwrite these values in [mmf_sa/forecasting_conf.yaml](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/forecasting_conf.yaml). 
+To modify the model hyperparameters or reset the range of the hyperparameter search, change the values in [mmf_sa/models/models_conf.yaml](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/models/models_conf.yaml) or overwrite these values, for example, in [mmf_sa/forecasting_conf_daily.yaml](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/forecasting_conf_daily.yaml) if your frequency is `D`. 
 
 MMF is fully integrated with MLflow and so once the training kicks off, the experiments will be visible in the MLflow Tracking UI with the corresponding metrics and parameters. Once the training is complete the models will be logged to MLFlow and registered to Unity Catalog. 
 
@@ -238,7 +238,7 @@ for model in active_models:
 
 Inside the [examples/run_daily.ipynb](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/run_daily.ipynb), we have the same ```run_forecast``` function as above. 
 
-To modify the model hyperparameters, change the values in [mmf_sa/models/models_conf.yaml](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/models/models_conf.yaml) or overwrite these values in [mmf_sa/forecasting_conf.yaml](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/forecasting_conf.yaml). 
+To modify the model hyperparameters, change the values in [mmf_sa/models/models_conf.yaml](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/models/models_conf.yaml) or overwrite these values, for example, in [mmf_sa/forecasting_conf_daily.yaml](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/forecasting_conf_daily.yaml) if your frequency is `D`. 
 
 MMF is fully integrated with MLflow and so once the training kicks off, the experiments will be visible in the MLflow Tracking UI with the corresponding metrics and parameters. During the evaluation, the models are logged and registered to Unity Catalog.
 

examples/daily/foundation_daily.ipynb

@@ -330,7 +330,7 @@
     "### Models\n",
     "Let's configure a list of models we are going to apply to our time series for evaluation and forecasting. A comprehensive list of all supported models is available in [mmf_sa/models/README.md](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/models/README.md). Look for the models where `model_type: foundation`; these are the foundation models we install from [chronos](https://pypi.org/project/chronos-forecasting/), [uni2ts](https://pypi.org/project/uni2ts/) and [timesfm](https://pypi.org/project/timesfm/). Check their documentation for the detailed description of each model.\n",
     "\n",
-    "Foundation time series models are pretrained on millions or billions of time series. These models can produce analysis (i.e. forecasting, anomaly detection, classfication) on an unforeseen time series without training or tuning. You can modify the hyperparameters in [mmf_sa/models/models_conf.yaml](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/models/models_conf.yaml) or overwrite the default values in [mmf_sa/forecasting_conf.yaml](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/forecasting_conf.yaml). You can also introduce new hyperparameters that are supported by the base models. To do this, first add those hyperparameters under the model specification in [mmf_sa/models/models_conf.yaml](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/models/models_conf.yaml). Then, include these hyperparameters inside the model instantiation which happens in the model pipeline script: e.g. `ChronosT5Tiny` class in [mmf_sa/models/chronosforecast/ChronosPipeline.py](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/models/chronosforecast/ChronosPipeline.py)."
+    "Foundation time series models are pretrained on millions or billions of time series. These models can produce analysis (i.e. forecasting, anomaly detection, classfication) on an unforeseen time series without training or tuning. You can modify the hyperparameters in [mmf_sa/models/models_conf.yaml](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/models/models_conf.yaml) or overwrite the default values in [mmf_sa/forecasting_conf_daily.yaml](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/forecasting_conf_daily.yaml). You can also introduce new hyperparameters that are supported by the base models. To do this, first add those hyperparameters under the model specification in [mmf_sa/models/models_conf.yaml](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/models/models_conf.yaml). Then, include these hyperparameters inside the model instantiation which happens in the model pipeline script: e.g. `ChronosT5Tiny` class in [mmf_sa/models/chronosforecast/ChronosPipeline.py](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/models/chronosforecast/ChronosPipeline.py)."
    ]
   },
   {

examples/daily/global_daily.ipynb

@@ -330,7 +330,7 @@
     "### Models\n",
     "Let's configure a list of models we are going to apply to our time series for evaluation and forecasting. A comprehensive list of all supported models is available in [mmf_sa/models/README.md](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/models/README.md). Look for the models where `model_type: global`; these are the global models we import from [neuralforecast](https://github.com/Nixtla/neuralforecast). Check their documentation for the detailed description of each model. \n",
     "\n",
-    "Some of these models perform [hyperparameter optimization](https://nixtlaverse.nixtla.io/neuralforecast/examples/automatic_hyperparameter_tuning.html) on its own to search for the best parameters. You can specify the range of the search or fix the values in [mmf_sa/models/models_conf.yaml](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/models/models_conf.yaml) or overwrite the default values in [mmf_sa/forecasting_conf.yaml](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/forecasting_conf.yaml). You can also introduce new hyperparameters that are supported by the base models. To do this, first add those hyperparameters under the model specification in [mmf_sa/models/models_conf.yaml](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/models/models_conf.yaml). Then, include these hyperparameters inside the model instantiation which happens in the model pipeline script: e.g. `NeuralFcAutoNBEATSx` class in [mmf_sa/models/neuralforecast/NeuralForecastPipeline.py](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/models/neuralforecast/NeuralForecastPipeline.py)."
+    "Some of these models perform [hyperparameter optimization](https://nixtlaverse.nixtla.io/neuralforecast/examples/automatic_hyperparameter_tuning.html) on its own to search for the best parameters. You can specify the range of the search or fix the values in [mmf_sa/models/models_conf.yaml](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/models/models_conf.yaml) or overwrite the default values in [mmf_sa/forecasting_conf_daily.yaml](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/forecasting_conf_daily.yaml). You can also introduce new hyperparameters that are supported by the base models. To do this, first add those hyperparameters under the model specification in [mmf_sa/models/models_conf.yaml](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/models/models_conf.yaml). Then, include these hyperparameters inside the model instantiation which happens in the model pipeline script: e.g. `NeuralFcAutoNBEATSx` class in [mmf_sa/models/neuralforecast/NeuralForecastPipeline.py](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/models/neuralforecast/NeuralForecastPipeline.py)."
    ]
   },
   {

examples/daily/local_univariate_daily.ipynb

@@ -381,7 +381,7 @@
     "### Models\n",
     "Let's configure a list of models we are going to apply to our time series for evaluation and forecasting. A comprehensive list of all supported models is available in [mmf_sa/models/README.md](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/models/README.md). Look for the models where `model_type: local`; these are the local models we import from [statsforecast](https://github.com/Nixtla/statsforecast) and [sktime](https://github.com/sktime/sktime). Check their documentations for the detailed description of each model. \n",
     "\n",
-    "Some of these models perform hyperparameter optimization ([statsforecast Automatic Forecasting](https://nixtlaverse.nixtla.io/statsforecast/index.html#automatic-forecasting)) on its own for some hyperparameters. For other hyperparameters or models, you can modify the hyperparameters in [mmf_sa/models/models_conf.yaml](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/models/models_conf.yaml) or overwrite the default values in [mmf_sa/forecasting_conf.yaml](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/forecasting_conf.yaml). You can also introduce new hyperparameters that are supported by the base models. To do this, first add those hyperparameters under the model specification in [mmf_sa/models/models_conf.yaml](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/models/models_conf.yaml). Then, include these hyperparameters inside the model instantiation which happens in the model pipeline script: e.g. `StatsFcAutoArima` class in [mmf_sa/models/statsforecast/StatsFcForecastingPipeline.py](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/models/statsforecast/StatsFcForecastingPipeline.py)."
+    "Some of these models perform hyperparameter optimization ([statsforecast Automatic Forecasting](https://nixtlaverse.nixtla.io/statsforecast/index.html#automatic-forecasting)) on its own for some hyperparameters. For other hyperparameters or models, you can modify the hyperparameters in [mmf_sa/models/models_conf.yaml](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/models/models_conf.yaml) or overwrite the default values in [mmf_sa/forecasting_conf_daily.yaml](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/forecasting_conf_daily.yaml). You can also introduce new hyperparameters that are supported by the base models. To do this, first add those hyperparameters under the model specification in [mmf_sa/models/models_conf.yaml](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/models/models_conf.yaml). Then, include these hyperparameters inside the model instantiation which happens in the model pipeline script: e.g. `StatsFcAutoArima` class in [mmf_sa/models/statsforecast/StatsFcForecastingPipeline.py](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/models/statsforecast/StatsFcForecastingPipeline.py)."
    ]
   },
   {