Merge pull request #90 from databricks-industry-solutions/reorganize-examples

Reorganize examples

Author: ryuta-yoshimatsu

README.md

@@ -12,10 +12,10 @@ Get started now!
 
 ## What's New
 
-- Jan 2025: [TimesFM](https://github.com/google-research/timesfm) is available for univariate and covariate forecasting. Try the notebooks: [univariate](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/foundation_daily.py) and [covariate](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/foundation_external_regressors_daily.py).
-- Jan 2025: [Chronos Bolt](https://github.com/amazon-science/chronos-forecasting) models are available for univariate forecasting. Try the [notebook](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/foundation_daily.py).
-- Jan 2025: [Moirai MoE](https://github.com/SalesforceAIResearch/uni2ts) models are available for univariate forecasting. Try the [notebook](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/foundation_daily.py).
-- Jan 2025: Added support for hourly (`freq="H"`) and weekly (`freq="W"`) time series . See the examples [here](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples).
+- Jan 2025: [TimesFM](https://github.com/google-research/timesfm) is available for univariate and covariate forecasting. Try the notebooks: [univariate](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/daily/foundation_daily.py) and [covariate](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/external_regressors/foundation_external_regressors_daily.py).
+- Jan 2025: [Chronos Bolt](https://github.com/amazon-science/chronos-forecasting) models are available for univariate forecasting. Try the [notebook](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/daily/foundation_daily.py).
+- Jan 2025: [Moirai MoE](https://github.com/SalesforceAIResearch/uni2ts) models are available for univariate forecasting. Try the [notebook](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/daily/foundation_daily.py).
+- Jan 2025: Added support for hourly (`freq="H"`) and weekly (`freq="W"`) time series . Try the notebooks: [hourly](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/hourly) and [weekly](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/weekly).
 
 ## Getting started
 
@@ -25,7 +25,7 @@ To run this solution on a public [M4](https://www.kaggle.com/datasets/yogesh94/m
 
 Local models are used to model individual time series. They could be advantageous over other types of model for their capabilities to tailor fit to individual series, offer greater interpretability, and require lower data requirements. We support models from [statsforecast](https://github.com/Nixtla/statsforecast), [r fable](https://cran.r-project.org/web/packages/fable/vignettes/fable.html) and [sktime](https://www.sktime.net/en/stable/). Covariates (i.e. exogenous regressors) are currently only supported for some models from statsforecast. 
 
-To get started, attach the [examples/local_univariate_daily.py](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/local_univariate_daily.py) notebook to a cluster running [DBR 14.3 LTS for ML](https://docs.databricks.com/en/release-notes/runtime/14.3lts-ml.html) or later versions. The cluster can be either a single-node or multi-node CPU cluster. Make sure to set the following [Spark configurations](https://spark.apache.org/docs/latest/configuration.html) on the cluster before you start using MMF: ```spark.sql.execution.arrow.enabled true``` and ```spark.sql.adaptive.enabled false``` (more detailed explanation to follow). 
+To get started, attach the [examples/daily/local_univariate_daily.py](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/daily/local_univariate_daily.py) notebook to a cluster running [DBR 15.4LTS for ML](https://docs.databricks.com/en/release-notes/runtime/15.4lts-ml.html) or later versions. The cluster can be either a single-node or multi-node CPU cluster. Make sure to set the following [Spark configurations](https://spark.apache.org/docs/latest/configuration.html) on the cluster before you start using MMF: ```spark.sql.execution.arrow.enabled true``` and ```spark.sql.adaptive.enabled false``` (more detailed explanation to follow). 
 
 In this notebook, we will apply 20+ models to 100 time series. You can specify the models to use in a list:
 
@@ -112,13 +112,13 @@ To modify the model hyperparameters, change the values in [mmf_sa/models/models_
 
 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.
 
-We encourage you to read through [examples/local_univariate_daily.py](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/local_univariate_daily.py) notebook to better understand how local models can be applied to your time series using MMF. Other example notebooks for monthly forecasting and forecasting with exogenous regressors can be found in [examples/local_univariate_monthly.py](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/local_univariate_monthly.py) and [examples/local_univariate_external_regressors_daily.py](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/local_univariate_external_regressors_daily.py).
+We encourage you to read through [examples/daily/local_univariate_daily.py](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/daily/local_univariate_daily.py) notebook to better understand how local models can be applied to your time series using MMF. An example notebook for forecasting with exogenous regressors can be found in [examples/external_regressors/local_univariate_external_regressors_daily.py](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/external_regressors/local_univariate_external_regressors_daily.py).
 
 ### Global Models
 
 Global models leverage patterns across multiple time series, enabling shared learning and improved predictions for each series. You would typically train one big model for many or all time series. They can often deliver better performance and robustness for forecasting large and similar datasets. We support deep learning based models from [neuralforecast](https://nixtlaverse.nixtla.io/neuralforecast/index.html). Covariates (i.e. exogenous regressors) and hyperparameter tuning are both supported for some models. 
 
-To get started, attach the [examples/global_daily.py](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/global_daily.py) notebook to a cluster running [DBR 14.3LTS for ML](https://docs.databricks.com/en/release-notes/runtime/index.html) or later version. We recommend using a single-node cluster with multiple GPU instances such as [g4dn.12xlarge [T4]](https://aws.amazon.com/ec2/instance-types/g4/) on AWS or [Standard_NC64as_T4_v3](https://learn.microsoft.com/en-us/azure/virtual-machines/nct4-v3-series) on Azure. Multi-node setting is currently not supported.
+To get started, attach the [examples/daily/global_daily.py](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/daily/global_daily.py) notebook to a cluster running [DBR 15.4LTS for ML](https://docs.databricks.com/en/release-notes/runtime/15.4lts-ml.html) or later version. We recommend using a single-node cluster with multiple GPU instances such as [g4dn.12xlarge [T4]](https://aws.amazon.com/ec2/instance-types/g4/) on AWS or [Standard_NC64as_T4_v3](https://learn.microsoft.com/en-us/azure/virtual-machines/nct4-v3-series) on Azure. Multi-node setting is currently not supported.
 
 You can choose the models to train and put them in a list:
 
@@ -139,7 +139,7 @@ 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).
 
-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 . 
+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. 
 
 ```python
 for model in active_models:
@@ -189,13 +189,13 @@ To modify the model hyperparameters or reset the range of the hyperparameter sea
 
 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. 
 
-We encourage you to read through [examples/global_daily.py](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/global_daily.py) notebook to better understand how global models can be applied to your time series using MMF. Other example notebooks for monthly forecasting and forecasting with exogenous regressors can be found in [examples/global_monthly.py](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/global_monthly.py) and [examples/global_external_regressors_daily.py](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/global_external_regressors_daily.py) respectively.
+We encourage you to read through [examples/daily/global_daily.py](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/daily/global_daily.py) notebook to better understand how global models can be applied to your time series using MMF. An example notebook for forecasting with exogenous regressors can be found in [examples/external_regressors/global_external_regressors_daily.py](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/external_regressors/global_external_regressors_daily.py).
 
 ### Foundation Models
 
-Foundation time series models are mostly transformer based models pretrained on millions or billions of time points. These models can perform analysis (i.e. forecasting, anomaly detection, classification) on a previously unseen time series without training or tuning. We support open source models from multiple sources: [chronos](https://github.com/amazon-science/chronos-forecasting), [timesfm](https://github.com/google-research/timesfm), and [moirai](https://blog.salesforceairesearch.com/moirai/). Covariates (i.e. exogenous regressors) and fine-tuning are currently not yet supported. This is a rapidly changing field, and we are working on updating the supported models and new features as the field evolves.
+Foundation time series models are mostly transformer based models pretrained on millions or billions of time points. These models can perform analysis (i.e. forecasting, anomaly detection, classification) on a previously unseen time series without training or tuning. We support open source models from multiple sources: [chronos](https://github.com/amazon-science/chronos-forecasting), [timesfm](https://github.com/google-research/timesfm), and [moirai](https://blog.salesforceairesearch.com/moirai/). This is a rapidly changing field, and we are working on updating the supported models and new features as the field evolves.
 
-To get started, attach the [examples/foundation_daily.py](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/foundation_daily.py) notebook to a cluster running [DBR 14.3 LTS for ML](https://docs.databricks.com/en/release-notes/runtime/index.html) or later versions. We recommend using a single-node cluster with multiple GPU instances such as [g4dn.12xlarge [T4]](https://aws.amazon.com/ec2/instance-types/g4/) on AWS or [Standard_NC64as_T4_v3](https://learn.microsoft.com/en-us/azure/virtual-machines/nct4-v3-series) on Azure. Multi-node setup is currently not supported. 
+To get started, attach the [examples/daily/foundation_daily.py](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/daily/foundation_daily.py) notebook to a cluster running [DBR 15.4LTS for ML](https://docs.databricks.com/en/release-notes/runtime/15.4lts-ml.html) or later versions. We recommend using a single-node cluster with multiple GPU instances such as [g4dn.12xlarge [T4]](https://aws.amazon.com/ec2/instance-types/g4/) on AWS or [Standard_NC64as_T4_v3](https://learn.microsoft.com/en-us/azure/virtual-machines/nct4-v3-series) on Azure. Multi-node setup is currently not supported. 
 
 You can choose the models you want to evaluate and forecast by specifying them in a list:
 
@@ -238,7 +238,7 @@ To modify the model hyperparameters, change the values in [mmf_sa/models/models_
 
 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.
 
-We encourage you to read through [examples/foundation_daily.py](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/foundation_daily.py) notebook to better understand how foundation models can be applied to your time series using MMF. An example notebook for monthly forecasting can be found in [examples/foundation_monthly.py](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/foundation_monthly.py).
+We encourage you to read through [examples/daily/foundation_daily.py](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/daily/foundation_daily.py) notebook to better understand how foundation models can be applied to your time series using MMF. An example notebook for forecasting with exogenous regressors can be found in [examples/external_regressors/foundation_external_regressors_daily.py](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/external_regressors/foundation_external_regressors_daily.py).
 
 #### Using Time Series Foundation Models on Databricks