Azure / Azure/MachineLearningNotebooks
Time series forecasting issue
- 主要語言
- Jupyter Notebook
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- PR 合併指標
- 30 天內沒有已合併 PR
描述
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
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研究方向
首先檢視所參照的階層式時間序列 notebook、連結的解決方案 notebook 和 PR #1627,以了解目前的工作流程與提案。在可以認定工作完成之前,需要決定 issue 的範圍,例如專注於預測改進、SDK 變更或服務設計。
由索引模型根據 Issue 內容生成。
評估
- 技術堆疊
- azure, jupyter-notebook, machine-learning, python
- 領域
- cloud, machine-learning
- Issue 類型
- 功能
- 難度
- 5/5
- 預估耗時
- 一週以上
- 活躍度
- 停滯
- 描述清晰度
- 需要釐清
- 新手友好度
- 18/100