Azure / Azure/MachineLearningNotebooks
Time series forecasting issue
- Lenguaje dominante
- Jupyter Notebook
- Estrellas
- 4.4k
- Forks
- 2.6k
- Métricas de merge de PR
- Sin PR fusionados en 30 d
Descripción
Hello,
@amlrelsa-ms, @jialiu103 ,I have a suggestion for Custom Hierarchical Time series forecasting
[Reproducing the steps as mentioned in the notebook ](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/automated-machine-learning/forecasting-hierarchical-timeseries/auto-ml-forecasting-hierarchical-timeseries.ipynb)with a compute resource of STANDARD_DS11_V2 over the [dataset](https://www.kaggle.com/c/walmart-recruiting-store-sales-forecasting) took more than 2 hours to train.
While I wanted a forecast for a very niche hierarchy combination (Store=1, Dept=2),I had to wait for the model to train on all hierarchy combinations,whose results weren't as important to me at that point in time.On top of that fetching predictions too was a very time consuming process and honestly slightly complicated.
Hence to address this issue to longer training and retrieveing results for what is needed at this point of time,I have created a calss based solution,which on run time dynamically slices the dataset on basis the heirarchy combination the user passes as paramter for creating a training job.There is also a caching feature to avoid retrainings.
I strongly feel that this could be incorporated as an Azure forecasting service which could work much like how the cognitive services work(by directly calling the api and fetching this results).Furthermore this could also be converted into a forecasting SDK.Infact I have the whole blueprint,designs and the flow diagram ready as to how the web interface could look like and how this can be extended furthermore with databases to store results,generating iframes for the forecasting charts that could be embedded elsewhere in a HTML page.
[This is the link to my solution notebook](https://github.com/Jash271/Experiments/blob/main/Azure/ML%20Studio/Custom%20Heirarchical%20Time%20Series%20Forecasting/Custom%20Heirarchical%20Time%20Series%20Forecasting.ipynb)
I know my code isn't really perfect,but I would love to know your feedback on where I could improve,or perhpas make this whole process even more optmized.
Incase if you'll do go ahead with this idea for the service or the sdk(I would love to contribute towards it's development).I just hope my solution happens to serve as a good starting point.
PR - @ #1627
Guía de contribución
No hay ninguna guía de contribución indexada para este repositorio
Línea de trabajo
Comienza revisando el notebook de series temporales jerárquicas mencionado, el notebook de solución enlazado y PR #1627 para comprender el flujo de trabajo actual y la propuesta. El issue necesita un alcance definido antes de que el trabajo pueda considerarse terminado, como una mejora específica de la predicción, un cambio en el SDK o un diseño del servicio.
Escrito por el modelo de indexación a partir del texto del issue.
Evaluación
- Stack tecnológico
- azure, jupyter-notebook, machine-learning, python
- Área
- cloud, machine-learning
- Tipo de issue
- Nueva funcionalidad
- Dificultad
- 5/5
- Tiempo estimado
- Más de una semana
- Estado de actividad
- Estancado
- Claridad
- Necesita aclaración
- Aptitud para principiantes
- 18/100