added-model-list

README.md

@@ -58,7 +58,7 @@ active_models = [
 ]
 ```
 
-A comprehensive list of local models currently supported by MMF is available in the [mmf_sa/models/models_conf.yaml](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/models/models_conf.yaml). 
+A comprehensive list of local models currently supported by MMF is available [here](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/models/README.md). 
 
 Now, run the forecasting using ```run_forecast``` function with the ```active_models``` list specified above:
 
@@ -140,7 +140,7 @@ active_models = [
 ]
 ```
 
-The models prefixed with "Auto" perform hyperparameter optimization within a specified range (see below for more detail). A comprehensive list of models currently supported by MMF is available in the [models_conf.yaml](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/models/models_conf.yaml).
+The models prefixed with "Auto" perform hyperparameter optimization within a specified range (see below for more detail). A comprehensive list of models currently supported by MMF is available [here](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/models/README.md).
 
 Now, with the following command, we run the [examples/run_daily.py](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/run_daily.py) notebook that will in turn call ```run_forecast``` function and loop through the ```active_models``` list. 
 
@@ -223,7 +223,7 @@ active_models = [
 ]
 ```
 
-A comprehensive list of models currently supported by MMF is available in the [models_conf.yaml](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/models/models_conf.yaml). 
+A comprehensive list of models currently supported by MMF is available [here](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/models/README.md). 
 
 Now, with the following command, we run [examples/run_daily.py](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/run_daily.py) notebook that will in turn run ```run_forecast``` function. We loop through the ```active_models``` list for the same reason mentioned above (see the global model section).
 

examples/daily/foundation_daily.py

@@ -108,7 +108,7 @@ def transform_group(df):
 # COMMAND ----------
 
 # MAGIC %md ### Models
-# MAGIC 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/models_conf.yaml](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/models/models_conf.yaml). 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.
+# MAGIC 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.
 # MAGIC
 # MAGIC 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).
 

examples/daily/global_daily.py

@@ -110,7 +110,7 @@ def transform_group(df):
 # COMMAND ----------
 
 # MAGIC %md ### Models
-# MAGIC 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/models_conf.yaml](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/models/models_conf.yaml). 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. 
+# MAGIC 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. 
 # MAGIC
 # MAGIC 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).
 

examples/daily/local_univariate_daily.py

@@ -125,7 +125,7 @@ def transform_group(df):
 # COMMAND ----------
 
 # MAGIC %md ### Models
-# MAGIC 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/models_conf.yaml](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/models/models_conf.yaml). Look for the models where `model_type: local`; these are the local models we import from [statsforecast](https://github.com/Nixtla/statsforecast), [r fable](https://cran.r-project.org/web/packages/fable/vignettes/fable.html) and [sktime](https://github.com/sktime/sktime). Check their documentations for the detailed description of each model. 
+# MAGIC 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), [r fable](https://cran.r-project.org/web/packages/fable/vignettes/fable.html) and [sktime](https://github.com/sktime/sktime). Check their documentations for the detailed description of each model. 
 # MAGIC
 # MAGIC 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).
 

examples/external_regressors/global_external_regressors_daily.py

@@ -95,7 +95,7 @@
 # COMMAND ----------
 
 # MAGIC %md ### Models
-# MAGIC 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/models_conf.yaml](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/models/models_conf.yaml). 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. 
+# MAGIC 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. 
 # MAGIC
 # MAGIC Exogenous regressors are currently only supported for [some models](https://nixtlaverse.nixtla.io/neuralforecast/models.html) (e.g. `NeuralForecastAutoNBEATSx`). The following list of models support exogenous regressors.
 

examples/external_regressors/local_univariate_external_regressors_daily.py

@@ -100,7 +100,7 @@
 # COMMAND ----------
 
 # MAGIC %md ### Models
-# MAGIC 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/models_conf.yaml](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/models/models_conf.yaml). Look for the models where `model_type: local`; these are the local models we import from [statsforecast](https://github.com/Nixtla/statsforecast), [r fable](https://cran.r-project.org/web/packages/fable/vignettes/fable.html) and [sktime](https://github.com/sktime/sktime). Check their documentations for the description of each model. 
+# MAGIC 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), [r fable](https://cran.r-project.org/web/packages/fable/vignettes/fable.html) and [sktime](https://github.com/sktime/sktime). Check their documentations for the description of each model. 
 # MAGIC
 # MAGIC Exogenous regressors are currently only supported for [some models](https://nixtlaverse.nixtla.io/statsforecast/index.html#models) from statsforecast (e.g. `StatsForecastAutoArima`). But including non-supported models in the active model list doesn't harm: models that can't use exogenous regressors will simply ignore them.
 

examples/hourly/foundation_hourly.py

@@ -112,7 +112,7 @@ def transform_group(df):
 # COMMAND ----------
 
 # MAGIC %md ### Models
-# MAGIC 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/models_conf.yaml](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/models/models_conf.yaml). 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. 
+# MAGIC 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. 
 
 # COMMAND ----------
 

examples/hourly/global_hourly.py

@@ -112,7 +112,7 @@ def transform_group(df):
 # COMMAND ----------
 
 # MAGIC %md ### Models
-# MAGIC 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/models_conf.yaml](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/models/models_conf.yaml). 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. 
+# MAGIC 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. 
 
 # COMMAND ----------
 

examples/hourly/local_univariate_hourly.py

@@ -119,7 +119,7 @@ def transform_group(df):
 # COMMAND ----------
 
 # MAGIC %md ### Models
-# MAGIC 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/models_conf.yaml](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/models/models_conf.yaml). Look for the models where `model_type: local`; these are the local models we import from [statsforecast](https://github.com/Nixtla/statsforecast). Check their documentations for the description of each model. 
+# MAGIC 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). Check their documentations for the description of each model. 
 # MAGIC
 # MAGIC *Note that hourly forecasting is currently not supported for `r fable` models.*
 

examples/monthly/foundation_monthly.py

@@ -121,7 +121,7 @@ def transform_group(df):
 # COMMAND ----------
 
 # MAGIC %md ### Models
-# MAGIC 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/models_conf.yaml](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/models/models_conf.yaml). 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.
+# MAGIC 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.
 
 # COMMAND ----------
 

examples/monthly/global_monthly.py

@@ -121,7 +121,7 @@ def transform_group(df):
 # COMMAND ----------
 
 # MAGIC %md ### Models
-# MAGIC 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/models_conf.yaml](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/models/models_conf.yaml). 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. 
+# MAGIC 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. 
 
 # COMMAND ----------