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Chronos: Move use-case to chronos-tutorial and modify page layout (#3868)

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* fix chronos deep_dive link error

* add use-case

* delete the use-case contained in chronos.md

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* modify the index.md format

* change page style

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* change use-case title

* fix anomaly detection link error
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docs/readthedocs/source/doc/Chronos/Overview/deep_dive.rst
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Chronos Deep Dive
=========
* `Time Series Processing and Feature Engineering <data_processing_feature_engineering.md>`__ introduces how to load a built-in/customized dataset and carry out transformation and feature engineering on it.
* `Time Series Forecasting <forecasting.md>`__ introduces how to build a time series forecasting application.
* `Time Series Anomaly Detection <anomaly_detection.md>`__ introduces how to build a anomaly detection application.
* `Generate Synthetic Sequential Data <simulation.md>`__ introduces how to build a series data generation application.
* `Useful Functionalities <useful_functionalities.md>`__ 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 <data_processing_feature_engineering.html>`__ introduces how to load a built-in/customized dataset and carry out transformation and feature engineering on it.
* `Time Series Forecasting <forecasting.html>`__ introduces how to build a time series forecasting application.
* `Time Series Anomaly Detection <anomaly_detection.html>`__ introduces how to build a anomaly detection application.
* `Generate Synthetic Sequential Data <simulation.html>`__ introduces how to build a series data generation application.
* `Useful Functionalities <useful_functionalities.html>`__ introduces some functionalities provided by Chronos that can help you improve accuracy/performance or scale the application to a larger data.
.. toctree::
:maxdepth: 1

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docs/readthedocs/source/doc/Chronos/QuickStart/index.md
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- [**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) &nbsp;![](../../../../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) &nbsp;![](../../../../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) &nbsp;![](../../../../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) &nbsp;![](../../../../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) &nbsp;![](../../../../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) &nbsp;![](../../../../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: *<https://github.com/jwkanggist/tf-keras-stock-pred>*
---------------------------
- [**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: *<https://facebook.github.io/prophet>*, *<https://github.com/jwkanggist/tf-keras-stock-pred>*
---------------------------
- [**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]: <https://bigdl.readthedocs.io/en/latest/doc/PythonAPI/Chronos/anomaly_detectors.html#dbscandetector>
[AE]: <https://bigdl.readthedocs.io/en/latest/doc/PythonAPI/Chronos/anomaly_detectors.html#aedetector>
[Threshold]: <https://bigdl.readthedocs.io/en/latest/doc/PythonAPI/Chronos/anomaly_detectors.html#thresholddetector>
[4]: <https://github.com/intel-analytics/bigdl/tree/branch-2.0/python/chronos/src/bigdl/chronos>
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.