From f682f791665893abba38a2140ffd324474a4baa5 Mon Sep 17 00:00:00 2001 From: Ruonan Wang <105281011+rnwang04@users.noreply.github.com> Date: Mon, 8 Aug 2022 14:28:29 +0800 Subject: [PATCH] Chronos : add multi-objective HPO with latency tutorial (#5307) * add mo hpo with latency tutorial * update according to comment * remove test_data * add license --- docs/readthedocs/source/doc/Chronos/QuickStart/index.md | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/docs/readthedocs/source/doc/Chronos/QuickStart/index.md b/docs/readthedocs/source/doc/Chronos/QuickStart/index.md index 1f736b67..2c5ff53c 100644 --- a/docs/readthedocs/source/doc/Chronos/QuickStart/index.md +++ b/docs/readthedocs/source/doc/Chronos/QuickStart/index.md @@ -106,6 +106,14 @@ 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. +--------------------------- + +- [**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]: [pytorch_forecasting_deepar]: [pytorch_forecasting_tft]: +[pytorch_forecasting_mo_tune]: \ No newline at end of file