From 584ee6045c84e400b74fc45ec6448d5e9ee8db3c Mon Sep 17 00:00:00 2001 From: Juntao_Luo <71260173+AndyLuo1029@users.noreply.github.com> Date: Tue, 23 Aug 2022 12:50:29 +0800 Subject: [PATCH] Chronos: update js, css and md files to add filter function in tutorial page. (#5506) --- .../source/_static/css/chronos_tutorial.css | 51 +++ .../source/_static/js/chronos_tutorial.js | 175 ++++++++ .../source/doc/Chronos/QuickStart/index.md | 374 +++++++++++------- 3 files changed, 464 insertions(+), 136 deletions(-) create mode 100644 docs/readthedocs/source/_static/css/chronos_tutorial.css create mode 100644 docs/readthedocs/source/_static/js/chronos_tutorial.js diff --git a/docs/readthedocs/source/_static/css/chronos_tutorial.css b/docs/readthedocs/source/_static/css/chronos_tutorial.css new file mode 100644 index 00000000000..a00498a171f --- /dev/null +++ b/docs/readthedocs/source/_static/css/chronos_tutorial.css @@ -0,0 +1,51 @@ +#tutorial img{ + margin: 10px 5px 10px 15px; +} + +.choiceline{ + display: flex; + justify-content: space-around; + margin: 0px 5px 10px 5px; +} + +.choicebox{ + width: 200px; + height: 20px; +} + +summary p{ + margin: 10px 0 0 0; + font-weight: bold; +} + +details p{ + padding: 5px 15px; + color: #404040; +} + +.border{ + background-color: #f3f6f6; + border: 1px solid rgba(0,0,0,.1); + color: #404040; + box-shadow: inset 0 1px 2px -1px hsl(0deg 0% 100% / 50%), inset 0 -2px 0 0 rgb(0 0 0 / 10%); + margin-bottom: 10px; + padding-top: 10px; + padding-left: 5px; + padding-right: 5px; + margin-top: -10px; + border-radius: 5px; + font-weight: bold; +} + +details p span{ + font-style: italic; + font-weight: bold; +} + +summary p button{ + border-radius: 2px; + border: 1px solid rgba(0,0,0,.1); + box-shadow: inset 0 1px 2px -1px hsl(0deg 0% 100% / 90%); + color: #404040; + font-family:Lato,proxima-nova,Helvetica Neue,Arial,sans-serif; +} \ No newline at end of file diff --git a/docs/readthedocs/source/_static/js/chronos_tutorial.js b/docs/readthedocs/source/_static/js/chronos_tutorial.js new file mode 100644 index 00000000000..b83db4de821 --- /dev/null +++ b/docs/readthedocs/source/_static/js/chronos_tutorial.js @@ -0,0 +1,175 @@ +$(document).ready(function(){ + $("#tutorial details").attr("open",true); +}); + +//func to show a tutorial +function showTutorials(ids){ + ids.forEach(id => { + $("#"+id).css("display","block"); + $("#"+id).attr("open","true"); + $("#"+id).next().css("display","block"); + }); +} + +//func to disable checkbox and button +function disCheck(ids){ + ids.forEach(id => { + $("#"+id).prop("disabled", true); + $("#"+id).parent().css("color","#c5c5c5"); + $("button[value='"+id+"']").prop("disabled",true); + $("button[value='"+id+"']").css("color","#c5c5c5"); + }); +} + +//event when click the checkboxes +$(".checkboxes").click(function(){ + //get all checked values + var vals = []; + $('input:checkbox:checked').each(function (index, item) { + vals.push($(this).val()); + }); + + //reset display + $("#tutorial details").css("display","none"); + $("#tutorial hr").css("display","none"); + //reset checkbox and button + $("#tutorial button").prop("disabled",false); + $("#tutorial input[type='checkbox']").prop("disabled",false); + $("#tutorial input[type='checkbox']").parent().css("color","#404040"); + $("#tutorial button").css("color","#404040"); + + //show tutorial according to checked values + if(vals.length==0){ + //choose noting, show all tutorials + $("#tutorial details").css("display","block"); + $("#tutorial details").attr("open",true); + $("#tutorial hr").css("display","block"); + } + //chose something, disable invalid checkboxes and buttons accordingly. + else if(vals.length==1){ + if(vals.includes("forecast")){ + var ids = ["ChronosForecaster","TuneaForecasting","AutoTSEstimator","AutoWIDE", + "MultvarWIDE","MultstepWIDE","LSTMForecaster","AutoProphet","AnomalyDetection", + "DeepARmodel","TFTmodel","hyperparameter","taxiDataset","distributedFashion", + "ONNX","Quantize","TCMFForecaster"]; + showTutorials(ids); + var disIds = ["simulation"]; + disCheck(disIds); + } + else if(vals.includes("anomaly_detection")){ + var ids = ["DetectAnomaly","Unsupervised","AnomalyDetection"]; + showTutorials(ids); + var disIds = ["simulation","hyperparameter_tuning","onnxruntime","quantization","distributed","customized_model"]; + disCheck(disIds); + } + else if(vals.includes("simulation")){ + var ids = ["SimualateTimeSeriesData"]; + showTutorials(ids); + var disIds = ["forecast","anomaly_detection","hyperparameter_tuning","onnxruntime","quantization","distributed","customized_model"]; + disCheck(disIds); + } + else if(vals.includes("hyperparameter_tuning")){ + var ids = ["TuneaForecasting","AutoTSEstimator","AutoWIDE","AutoProphet", + "hyperparameter","taxiDataset","ONNX"]; + showTutorials(ids); + var disIds = ["anomaly_detection","simulation","quantization","distributed"]; + disCheck(disIds); + } + else if(vals.includes("onnxruntime")){ + var ids = ["ONNX"]; + showTutorials(ids); + var disIds = ["anomaly_detection","simulation","quantization","distributed","customized_model"]; + disCheck(disIds); + } + else if(vals.includes("quantization")){ + var ids = ["Quantize"]; + showTutorials(ids); + var disIds = ["anomaly_detection","simulation","hyperparameter_tuning","onnxruntime","distributed","customized_model"]; + disCheck(disIds); + } + else if(vals.includes("distributed")){ + var ids = ["distributedFashion","TCMFForecaster"]; + showTutorials(ids); + var disIds = ["anomaly_detection","simulation","hyperparameter_tuning","onnxruntime","quantization","customized_model"]; + disCheck(disIds); + } + else if(vals.includes("customized_model")){ + var ids = ["AutoTSEstimator","DeepARmodel","TFTmodel"]; + showTutorials(ids); + var disIds = ["anomaly_detection","simulation","onnxruntime","quantization","distributed"]; + disCheck(disIds); + } + } + else if(vals.length==2){ + if(vals.includes("forecast") && vals.includes("hyperparameter_tuning")){ + var ids = ["TuneaForecasting","AutoTSEstimator","AutoWIDE","AutoProphet","hyperparameter","taxiDataset","ONNX","AutoTSEstimator"]; + showTutorials(ids); + var disIds = ["anomaly_detection","simulation","quantization","distributed"]; + disCheck(disIds); + } + else if(vals.includes("forecast") && vals.includes("anomaly_detection")){ + var ids = ["AnomalyDetection"]; + showTutorials(ids); + var disIds = ["simulation","hyperparameter_tuning","onnxruntime","quantization","distributed","customized_model"]; + disCheck(disIds); + } + else if(vals.includes("forecast") && vals.includes("customized_model")){ + var ids = ["DeepARmodel","TFTmodel","AutoTSEstimator"]; + showTutorials(ids); + var disIds = ["anomaly_detection","simulation","onnxruntime","quantization","distributed"]; + disCheck(disIds); + } + else if(vals.includes("forecast") && vals.includes("distributed")){ + var ids = ["distributedFashion","TCMFForecaster"]; + showTutorials(ids); + var disIds = ["anomaly_detection","simulation","hyperparameter_tuning","onnxruntime","quantization","customized_model"]; + disCheck(disIds); + } + else if(vals.includes("forecast") && vals.includes("quantization")){ + var ids = ["Quantize"]; + showTutorials(ids); + var disIds = ["anomaly_detection","simulation","hyperparameter_tuning","onnxruntime","distributed","customized_model"]; + disCheck(disIds); + } + else if(vals.includes("hyperparameter_tuning") && vals.includes("customized_model")){ + var ids = ["AutoTSEstimator"]; + showTutorials(ids); + var disIds = ["anomaly_detection","simulation","onnxruntime","quantization","distributed"]; + disCheck(disIds); + } + else if(vals.includes("forecast") && vals.includes("onnxruntime")){ + var ids = ["ONNX"]; + showTutorials(ids); + var disIds = ["anomaly_detection","simulation","quantization","distributed","customized_model"]; + disCheck(disIds); + } + else if(vals.includes("hyperparameter_tuning") && vals.includes("onnxruntime")){ + var ids = ["ONNX"]; + showTutorials(ids); + var disIds = ["anomaly_detection","simulation","quantization","distributed","customized_model"]; + disCheck(disIds); + } + } + else if(vals.length==3){ + if(vals.includes("forecast") && vals.includes("hyperparameter_tuning") && vals.includes("customized_model")){ + var ids = ["AutoTSEstimator"]; + showTutorials(ids); + var disIds = ["anomaly_detection","simulation","onnxruntime","quantization","distributed"]; + disCheck(disIds); + } + else if(vals.includes("forecast") && vals.includes("hyperparameter_tuning") && vals.includes("onnxruntime")){ + var ids = ["ONNX"]; + showTutorials(ids); + var disIds = ["anomaly_detection","simulation","quantization","distributed","customized_model"]; + disCheck(disIds); + } + } +}); + +//event when click the tags' buttons +$("details p button").click(function(){ + var id = $(this).val(); + $("#"+id).trigger("click"); +}); + +// var allIds = ["forecast","anomaly_detection","simulation","hyperparameter_tuning","onnxruntime","quantization","distributed","customized_model"]; \ No newline at end of file diff --git a/docs/readthedocs/source/doc/Chronos/QuickStart/index.md b/docs/readthedocs/source/doc/Chronos/QuickStart/index.md index 2c5ff53c1dc..7b1b2fc5ed0 100644 --- a/docs/readthedocs/source/doc/Chronos/QuickStart/index.md +++ b/docs/readthedocs/source/doc/Chronos/QuickStart/index.md @@ -1,138 +1,240 @@ # Chronos Tutorial -- [**Predict Number of Taxi Passengers with Chronos Forecaster**](./chronos-tsdataset-forecaster-quickstart.html) - - > ![](../../../../image/colab_logo_32px.png)[Run in Google Colab][chronos_nyc_taxi_tsdataset_forecaster_colab]  ![](../../../../image/GitHub-Mark-32px.png)[View source on GitHub][chronos_nyc_taxi_tsdataset_forecaster] - - In this guide we will demonstrate how to use _Chronos TSDataset_ and _Chronos Forecaster_ for time series processing and predict number of taxi passengers. - ---------------------------- - -- [**Tune a Forecasting Task Automatically**](./chronos-autotsest-quickstart.html) - - > ![](../../../../image/colab_logo_32px.png)[Run in Google Colab][chronos_autots_nyc_taxi_colab]  ![](../../../../image/GitHub-Mark-32px.png)[View source on GitHub][chronos_autots_nyc_taxi] - - In this guide we will demonstrate how to use _Chronos AutoTSEstimator_ and _Chronos TSPipeline_ to auto tune a time seires forecasting task and handle the whole model development process easily. - ---------------------------- - -- [**Detect Anomaly Point in Real Time Traffic Data**](./chronos-anomaly-detector.html) - - > ![](../../../../image/colab_logo_32px.png)[Run in Google Colab][chronos_minn_traffic_anomaly_detector_colab]  ![](../../../../image/GitHub-Mark-32px.png)[View source on GitHub][chronos_minn_traffic_anomaly_detector] - - In this guide we will demonstrate how to use _Chronos Anomaly Detector_ for real time traffic data from the Twin Cities Metro area in Minnesota anomaly detection. - ---------------------------- - -- [**Tune a Customized Time Series Forecasting Model with AutoTSEstimator.**][network_traffic_autots_customized_model] - - > ![](../../../../image/GitHub-Mark-32px.png)[View source on GitHub][network_traffic_autots_customized_model] - - In this notebook, we demonstrate a reference use case where we use the network traffic KPI(s) in the past to predict traffic KPI(s) in the future. We demonstrate how to use _AutoTSEstimator_ to adjust the parameters of a customized model. - ---------------------------- - -- [**Auto Tune the Prediction of Network Traffic at the Transit Link of WIDE**][network_traffic_autots_forecasting] - - > ![](../../../../image/GitHub-Mark-32px.png)[View source on GitHub][network_traffic_autots_forecasting] - - In this notebook, we demostrate a reference use case where we use the network traffic KPI(s) in the past to predict traffic KPI(s) in the future. We demostrate how to use _AutoTS_ in project [Chronos][chronos] to do time series forecasting in an automated and distributed way. - ---------------------------- - -- [**Multivariate Forecasting of Network Traffic at the Transit Link of WIDE**][network_traffic_model_forecasting] - - > ![](../../../../image/GitHub-Mark-32px.png)[View source on GitHub][network_traffic_model_forecasting] - - In this notebook, we demonstrate a reference use case where we use the network traffic KPI(s) in the past to predict traffic KPI(s) in the future. We demostrate how to do univariate forecasting (predict only 1 series), and multivariate forecasting (predicts more than 1 series at the same time) using Project [Chronos][chronos]. - ---------------------------- - -- [**Multistep Forecasting of Network Traffic at the Transit Link of WIDE**][network_traffic_multivariate_multistep_tcnforecaster] - - > ![](../../../../image/GitHub-Mark-32px.png)[View source on GitHub][network_traffic_multivariate_multistep_tcnforecaster] - - In this notebook, we demonstrate a reference use case where we use the network traffic KPI(s) in the past to predict traffic KPI(s) in the future. We demostrate how to do multivariate multistep forecasting using Project [Chronos][chronos]. - ---------------------------- - -- [**Stock Price Prediction with LSTMForecaster**][stock_prediction] - - > ![](../../../../image/GitHub-Mark-32px.png)[View source on GitHub][stock_prediction] - - In this notebook, we demonstrate a reference use case where we use historical stock price data to predict the future price. The dataset we use is the daily stock price of S&P500 stocks during 2013-2018 (data source). We demostrate how to do univariate forecasting using the past 80% of the total days' MMM price to predict the future 20% days' daily price. - - Reference: ** - ---------------------------- - -- [**Stock Price Prediction with ProphetForecaster and AutoProphet**][stock_prediction_prophet] - - > ![](../../../../image/GitHub-Mark-32px.png)[View source on GitHub][stock_prediction_prophet] - - In this notebook, we demonstrate a reference use case where we use historical stock price data to predict the future price using the ProphetForecaster and AutoProphet. The dataset we use is the daily stock price of S&P500 stocks during 2013-2018 [data source](https://www.kaggle.com/camnugent/sandp500/). - - Reference: **, ** - ---------------------------- - -- [**Unsupervised Anomaly Detection for CPU Usage**][AIOps_anomaly_detect_unsupervised] - - > ![](../../../../image/GitHub-Mark-32px.png)[View source on GitHub][AIOps_anomaly_detect_unsupervised] - - We demonstrates how to perform anomaly detection based on Chronos's built-in [DBScanDetector][DBScan], [AEDetector][AE] and [ThresholdDetector][Threshold]. - ---------------------------- - -- [**Anomaly Detection for CPU Usage Based on Forecasters**][AIOps_anomaly_detect_unsupervised_forecast_based] - - > ![](../../../../image/GitHub-Mark-32px.png)[View source on GitHub][AIOps_anomaly_detect_unsupervised_forecast_based] - - We demonstrates how to leverage Chronos's built-in models ie. MTNet, to do time series forecasting. Then perform anomaly detection on predicted value with [ThresholdDetector][Threshold]. - ---------------------------- - -- [**Help pytorch-forecasting improve the training speed of DeepAR model**][pytorch_forecasting_deepar] - - > ![](../../../../image/GitHub-Mark-32px.png)[View source on GitHub][pytorch_forecasting_deepar] - - 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 DeepAR model. - ---------------------------- - -- [**Help pytorch-forecasting improve the training speed of TFT model**][pytorch_forecasting_tft] - - > ![](../../../../image/GitHub-Mark-32px.png)[View source on GitHub][pytorch_forecasting_tft] - - 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> -[Threshold]: <../../PythonAPI/Chronos/anomaly_detectors.html#thresholddetector> -[chronos]: -[chronos_nyc_taxi_tsdataset_forecaster_colab]: -[chronos_nyc_taxi_tsdataset_forecaster]: -[chronos_autots_nyc_taxi_colab]: -[chronos_autots_nyc_taxi]: -[chronos_minn_traffic_anomaly_detector_colab]: -[chronos_minn_traffic_anomaly_detector]: -[network_traffic_autots_customized_model]: -[network_traffic_autots_forecasting]: -[network_traffic_model_forecasting]: -[network_traffic_multivariate_multistep_tcnforecaster]: -[stock_prediction]: -[stock_prediction_prophet]: -[AIOps_anomaly_detect_unsupervised]: -[AIOps_anomaly_detect_unsupervised_forecast_based]: -[pytorch_forecasting_deepar]: -[pytorch_forecasting_tft]: -[pytorch_forecasting_mo_tune]: \ No newline at end of file +
+ +```eval_rst +.. raw:: html + + + +
+

Filter:

+

Please check the checkboxes or click tag buttons to show the related tutorials. Reclick or uncheck will hide corresponding tutorials. If nothing is checked or clicked, all the tutorials will be displayed.

+
+
+
forecast
+
anomaly detection
+
simulation
+
AutoML
+
+
+
onnxruntime
+
quantization
+
distributed
+
customized model
+
+
+
+ +
+ + Predict Number of Taxi Passengers with Chronos Forecaster +

Tag:

+
+ Run in Google Colab +   + View source on GitHub +

In this guide we will demonstrate how to use Chronos TSDataset and Chronos Forecaster for time series processing and predict number of taxi passengers.

+
+
+ +
+ + Tune a Forecasting Task Automatically +

Tag:  

+
+ Run in Google Colab +   + View source on GitHub +

In this guide we will demonstrate how to use Chronos AutoTSEstimator and Chronos TSPipeline to auto tune a time seires forecasting task and handle the whole model development process easily.

+
+
+ +
+ + Detect Anomaly Point in Real Time Traffic Data +

Tag:

+
+ Run in Google Colab +   + View source on GitHub +

In this guide we will demonstrate how to use Chronos Anomaly Detector for real time traffic data from the Twin Cities Metro area in Minnesota anomaly detection.

+
+
+ +
+ + Tune a Customized Time Series Forecasting Model with AutoTSEstimator. +

Tag:   

+
+ View source on GitHub +

In this notebook, we demonstrate a reference use case where we use the network traffic KPI(s) in the past to predict traffic KPI(s) in the future. We demonstrate how to use AutoTSEstimator to adjust the parameters of a customized model.

+
+
+ +
+ + Auto Tune the Prediction of Network Traffic at the Transit Link of WIDE +

Tag:  

+
+ View source on GitHub +

In this notebook, we demostrate a reference use case where we use the network traffic KPI(s) in the past to predict traffic KPI(s) in the future. We demostrate how to use AutoTS in project Chronos to do time series forecasting in an automated and distributed way.

+
+
+ +
+ + Multivariate Forecasting of Network Traffic at the Transit Link of WIDE +

Tag:

+
+ View source on GitHub +

In this notebook, we demonstrate a reference use case where we use the network traffic KPI(s) in the past to predict traffic KPI(s) in the future. We demostrate how to do univariate forecasting (predict only 1 series), and multivariate forecasting (predicts more than 1 series at the same time) using Project Chronos.

+
+
+ +
+ + Multistep Forecasting of Network Traffic at the Transit Link of WIDE +

Tag:

+
+ View source on GitHub +

In this notebook, we demonstrate a reference use case where we use the network traffic KPI(s) in the past to predict traffic KPI(s) in the future. We demostrate how to do multivariate multistep forecasting using Project Chronos.

+
+
+ +
+ + Stock Price Prediction with LSTMForecaster +

Tag:

+
+ View source on GitHub +

In this notebook, we demonstrate a reference use case where we use historical stock price data to predict the future price. The dataset we use is the daily stock price of S&P500 stocks during 2013-2018 (data source). We demostrate how to do univariate forecasting using the past 80% of the total days' MMM price to predict the future 20% days' daily price.

+

Reference: https://github.com/jwkanggist/tf-keras-stock-pred

+
+
+ +
+ + Stock Price Prediction with ProphetForecaster and AutoProphet +

Tag:  

+
+ View source on GitHub +

In this notebook, we demonstrate a reference use case where we use historical stock price data to predict the future price using the ProphetForecaster and AutoProphet. The dataset we use is the daily stock price of S&P500 stocks during 2013-2018 data source.

+

Reference: https://facebook.github.io/prophet, https://github.com/jwkanggist/tf-keras-stock-pred

+
+
+ +
+ + Unsupervised Anomaly Detection for CPU Usage +

Tag:

+
+ View source on GitHub +

We demonstrates how to perform anomaly detection based on Chronos's built-in DBScanDetector, AEDetector and ThresholdDetector.

+
+
+ +
+ + Anomaly Detection for CPU Usage Based on Forecasters +

Tag:  

+
+ View source on GitHub +

We demonstrates how to leverage Chronos's built-in models ie. MTNet, to do time series forecasting. Then perform anomaly detection on predicted value with ThresholdDetector.

+
+
+ +
+ + Help pytorch-forecasting improve the training speed of DeepAR model +

Tag:  

+
+ View source on GitHub +

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 DeepAR model.

+
+
+ +
+ + Help pytorch-forecasting improve the training speed of TFT model +

Tag:  

+
+ View source on GitHub +

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. +

Tag:  

+
+ View source on GitHub +

In this notebook, we demostrate how to use multi-objective hyperparameter optimization with built-in latency metric in project Chronos to do time series forecasting and achieve good tradeoff between performance and latency.

+
+
+ +
+ + Auto tuning prophet on nyc taxi dataset +

Tag:  

+
+ View source on GitHub +

This example collection will demonstrate Chronos auto models (i.e. autolstm & autoprophet) perform automatic time series forecasting on nyc_taxi dataset. The auto model will search the best hyperparameters automatically.

+
+
+ +
+ + Use Chronos forecasters in a distributed fashion +

Tag:  

+
+ View source on GitHub +

Users can easily train their forecasters in a distributed fashion to handle extra large dataset and speed up the process (training and data processing) by utilizing a cluster or pseudo-distribution on a single node. The functionality is powered by Project Orca.

+
+
+ +
+ + Use ONNXRuntime to speed-up forecasters' inferecing +

Tag:   

+
+ View source on GitHub +

This example will demonstrate how to use ONNX to speed up the inferencing(prediction/evalution) on forecasters and AutoTSEstimator. In this example, onnx speed up the inferencing for ~4X.

+
+
+ +
+ + Quantize Chronos forecasters method to speed-up inference +

Tag:  

+
+ View source on GitHub +

Users can easily quantize their forecasters to low precision and speed up the inference process (both throughput and latency) by on a single node. The functionality is powered by Project Nano.

+
+
+ +
+ + Simualate time series data with similar pattern as example data +

Tag:

+
+ View source on GitHub +

This example shows how to generate synthetic data with similar distribution as training data with the fast and easy DPGANSimulator API provided by Chronos.

+
+
+ +
+ + High dimension time series forecasting with Chronos TCMFForecaster +

Tag:  

+
+ View source on GitHub +

This example demonstrates how to use BigDL Chronos TCMFForecaster to run distributed training and inference for high dimension time series forecasting task.

+
+
+ +
+ + +``` \ No newline at end of file