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Chronos : add multi-objective HPO with latency tutorial (#5307)

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* add mo hpo with latency tutorial

* update according to comment

* remove test_data

* add license
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Ruonan Wang 2022-08-08 14:28:29 +08:00 committed by GitHub
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docs/readthedocs/source/doc/Chronos/QuickStart/index.md
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Chronos can help a 3rd party time series lib to improve the performance (both training and inferencing) and accuracy. This use-case shows Chronos can easily help pytorch-forecasting speed up the training of TFT model.
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- [**Tune a Time Series Forecasting Model with multi-objective hyperparameter optimization.**][pytorch_forecasting_mo_tune]
> ![](../../../../image/GitHub-Mark-32px.png)[View source on GitHub][pytorch_forecasting_mo_tune]
In this notebook, we demostrate how to use _multi-objective hyperparameter optimization with built-in latency metric_ in project [Chronos][chronos] to do time series forecasting and achieve good tradeoff between performance and latency.
[DBScan]: <../../PythonAPI/Chronos/anomaly_detectors.html#dbscandetector>
[AE]: <../../PythonAPI/Chronos/anomaly_detectors.html#aedetector>
@ -127,3 +135,4 @@
[AIOps_anomaly_detect_unsupervised_forecast_based]: <https://github.com/intel-analytics/BigDL/blob/main/python/chronos/use-case/AIOps/AIOps_anomaly_detect_unsupervised_forecast_based.ipynb>
[pytorch_forecasting_deepar]: <https://github.com/intel-analytics/BigDL/tree/main/python/chronos/use-case/pytorch-forecasting/DeepAR>
[pytorch_forecasting_tft]: <https://github.com/intel-analytics/BigDL/tree/main/python/chronos/use-case/pytorch-forecasting/TFT>
[pytorch_forecasting_mo_tune]: <https://github.com/intel-analytics/BigDL/tree/main/python/chronos/example/hpo/muti_objective_hpo_with_builtin_latency_tutorial.ipynb>