From ad47a0b8e0ee65e56af17d9b4f4c024223caffe2 Mon Sep 17 00:00:00 2001 From: liangs6212 <80952198+liangs6212@users.noreply.github.com> Date: Thu, 13 Jan 2022 11:11:40 +0800 Subject: [PATCH] Chronos: Move use-case to `chronos-tutorial` and modify page layout (#3868) * fix chronos deep_dive link error * add use-case * delete the use-case contained in chronos.md * add space * modify the index.md format * change page style * fix link error * change title link * fix repo error * change to relative path * remove extra symbols * add \n * change use-case title * fix anomaly detection link error --- .../source/doc/Chronos/Overview/deep_dive.rst | 10 +-- .../source/doc/Chronos/QuickStart/index.md | 87 ++++++++++++++++++- 2 files changed, 88 insertions(+), 9 deletions(-) diff --git a/docs/readthedocs/source/doc/Chronos/Overview/deep_dive.rst b/docs/readthedocs/source/doc/Chronos/Overview/deep_dive.rst index b736fa64..11fa4673 100644 --- a/docs/readthedocs/source/doc/Chronos/Overview/deep_dive.rst +++ b/docs/readthedocs/source/doc/Chronos/Overview/deep_dive.rst @@ -1,11 +1,11 @@ Chronos Deep Dive ========= -* `Time Series Processing and Feature Engineering `__ introduces how to load a built-in/customized dataset and carry out transformation and feature engineering on it. -* `Time Series Forecasting `__ introduces how to build a time series forecasting application. -* `Time Series Anomaly Detection `__ introduces how to build a anomaly detection application. -* `Generate Synthetic Sequential Data `__ introduces how to build a series data generation application. -* `Useful Functionalities `__ introduces some functionalities provided by Chronos that can help you improve accuracy/performance or scale the application to a larger data. +* `Time Series Processing and Feature Engineering `__ introduces how to load a built-in/customized dataset and carry out transformation and feature engineering on it. +* `Time Series Forecasting `__ introduces how to build a time series forecasting application. +* `Time Series Anomaly Detection `__ introduces how to build a anomaly detection application. +* `Generate Synthetic Sequential Data `__ introduces how to build a series data generation application. +* `Useful Functionalities `__ introduces some functionalities provided by Chronos that can help you improve accuracy/performance or scale the application to a larger data. .. toctree:: :maxdepth: 1 diff --git a/docs/readthedocs/source/doc/Chronos/QuickStart/index.md b/docs/readthedocs/source/doc/Chronos/QuickStart/index.md index d493cb04..b3e719ea 100644 --- a/docs/readthedocs/source/doc/Chronos/QuickStart/index.md +++ b/docs/readthedocs/source/doc/Chronos/QuickStart/index.md @@ -2,18 +2,97 @@ - [**Predict Number of Taxi Passengers with Chronos Forecaster**](./chronos-tsdataset-forecaster-quickstart.html) - ![](../../../../image/colab_logo_32px.png)[Run in Google Colab](https://colab.research.google.com/github/intel-analytics/BigDL/blob/branch-2.0/python/chronos/colab-notebook/chronos_nyc_taxi_tsdataset_forecaster.ipynb)  ![](../../../../image/GitHub-Mark-32px.png)[View source on GitHub](https://github.com/intel-analytics/BigDL/blob/branch-2.0/python/chronos/colab-notebook/chronos_nyc_taxi_tsdataset_forecaster.ipynb) + > ![](../../../../image/colab_logo_32px.png)[Run in Google Colab](https://colab.research.google.com/github/intel-analytics/BigDL/blob/branch-2.0/python/chronos/colab-notebook/chronos_nyc_taxi_tsdataset_forecaster.ipynb)  ![](../../../../image/GitHub-Mark-32px.png)[View source on GitHub](https://github.com/intel-analytics/BigDL/blob/branch-2.0/python/chronos/colab-notebook/chronos_nyc_taxi_tsdataset_forecaster.ipynb) 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](https://colab.research.google.com/github/intel-analytics/BigDL/blob/branch-2.0/python/chronos/colab-notebook/chronos_autots_nyc_taxi.ipynb)  ![](../../../../image/GitHub-Mark-32px.png)[View source on GitHub](https://github.com/intel-analytics/BigDL/blob/branch-2.0/python/chronos/colab-notebook/chronos_autots_nyc_taxi.ipynb) + > ![](../../../../image/colab_logo_32px.png)[Run in Google Colab](https://colab.research.google.com/github/intel-analytics/BigDL/blob/branch-2.0/python/chronos/colab-notebook/chronos_autots_nyc_taxi.ipynb)  ![](../../../../image/GitHub-Mark-32px.png)[View source on GitHub](https://github.com/intel-analytics/BigDL/blob/branch-2.0/python/chronos/colab-notebook/chronos_autots_nyc_taxi.ipynb) 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](https://colab.research.google.com/github/intel-analytics/BigDL/blob/branch-2.0/python/chronos/colab-notebook/chronos_minn_traffic_anomaly_detector.ipynb)  ![](../../../../image/GitHub-Mark-32px.png)[View source on GitHub](https://github.com/intel-analytics/BigDL/blob/branch-2.0/python/chronos/colab-notebook/chronos_minn_traffic_anomaly_detector.ipynb) + > ![](../../../../image/colab_logo_32px.png)[Run in Google Colab](https://colab.research.google.com/github/intel-analytics/BigDL/blob/branch-2.0/python/chronos/colab-notebook/chronos_minn_traffic_anomaly_detector.ipynb)  ![](../../../../image/GitHub-Mark-32px.png)[View source on GitHub](https://github.com/intel-analytics/BigDL/blob/branch-2.0/python/chronos/colab-notebook/chronos_minn_traffic_anomaly_detector.ipynb) + + 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.**](https://github.com/intel-analytics/BigDL/blob/branch-2.0/python/chronos/use-case/network_traffic/network_traffic_autots_customized_model.ipynb) + + > ![](../../../../image/GitHub-Mark-32px.png)[View source on GitHub](https://github.com/intel-analytics/BigDL/blob/branch-2.0/python/chronos/use-case/network_traffic/network_traffic_autots_customized_model.ipynb) + + 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**](https://github.com/intel-analytics/BigDL/blob/branch-2.0/python/chronos/use-case/network_traffic/network_traffic_autots_forecasting.ipynb) + + > ![](../../../../image/GitHub-Mark-32px.png)[View source on GitHub](https://github.com/intel-analytics/BigDL/blob/branch-2.0/python/chronos/use-case/network_traffic/network_traffic_autots_forecasting.ipynb) + + 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][4] to do time series forecasting in an automated and distributed way. + +--------------------------- + +- [**Multivariate Forecasting of Network Traffic at the Transit Link of WIDE**](https://github.com/intel-analytics/BigDL/blob/branch-2.0/python/chronos/use-case/network_traffic/network_traffic_model_forecasting.ipynb) + + > ![](../../../../image/GitHub-Mark-32px.png)[View source on GitHub](https://github.com/intel-analytics/BigDL/blob/branch-2.0/python/chronos/use-case/network_traffic/network_traffic_model_forecasting.ipynb) + + 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][4]. + +--------------------------- + +- [**Multistep Forecasting of Network Traffic at the Transit Link of WIDE**](https://github.com/intel-analytics/BigDL/blob/branch-2.0/python/chronos/use-case/network_traffic/network_traffic_multivariate_multistep_tcnforecaster.ipynb) + + > ![](../../../../image/GitHub-Mark-32px.png)[View source on GitHub](https://github.com/intel-analytics/BigDL/blob/branch-2.0/python/chronos/use-case/network_traffic/network_traffic_multivariate_multistep_tcnforecaster.ipynb) + + 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][4]. + +--------------------------- + +- [**Stock Price Prediction with LSTMForecaster**](https://github.com/intel-analytics/BigDL/blob/branch-2.0/python/chronos/use-case/fsi/stock_prediction.ipynb) + + > ![](../../../../image/GitHub-Mark-32px.png)[View source on GitHub](https://github.com/intel-analytics/BigDL/blob/branch-2.0/python/chronos/use-case/fsi/stock_prediction.ipynb) + + 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**](https://github.com/intel-analytics/BigDL/blob/branch-2.0/python/chronos/use-case/fsi/stock_prediction_prophet.ipynb) + + > ![](../../../../image/GitHub-Mark-32px.png)[View source on GitHub](https://github.com/intel-analytics/BigDL/blob/branch-2.0/python/chronos/use-case/fsi/stock_prediction_prophet.ipynb) + + 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**](https://github.com/intel-analytics/BigDL/blob/branch-2.0/python/chronos/use-case/AIOps/AIOps_anomaly_detect_unsupervised.ipynb) + + > ![](../../../../image/GitHub-Mark-32px.png)[View source on GitHub](https://github.com/intel-analytics/BigDL/blob/branch-2.0/python/chronos/use-case/AIOps/AIOps_anomaly_detect_unsupervised.ipynb) + + 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**](https://github.com/intel-analytics/BigDL/blob/branch-2.0/python/chronos/use-case/AIOps/AIOps_anomaly_detect_unsupervised_forecast_based.ipynb) + + > ![](../../../../image/GitHub-Mark-32px.png)[View source on GitHub](https://github.com/intel-analytics/BigDL/blob/branch-2.0/python/chronos/use-case/AIOps/AIOps_anomaly_detect_unsupervised_forecast_based.ipynb) + + 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]. + + +[DBScan]: +[AE]: +[Threshold]: +[4]: - 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. \ No newline at end of file