Chronos: Refactor document structure (#3839)

* add strcture change

* add change in index

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* revice the document

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* remove ood information

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Junwei Deng 2022-01-08 16:40:46 +08:00 committed by GitHub
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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.
.. toctree::
:maxdepth: 1
:hidden:
data_processing_feature_engineering.md
forecasting.md
anomaly_detection.md
simulation.md
useful_functionalities.md

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# Anomaly Detector Quickstart # Detect Anomaly Point in Real Time Traffic Data
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# AutoTSEstimator Quickstart # Tune a Forecasting Task Automatically
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# TSDataset and Forecaster Quickstarts # Predict Number of Taxi Passengers with Chronos Forecaster
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# Chronos Tutorial
- [**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)
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)
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)
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.

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:caption: Chronos Overview :caption: Chronos Overview
doc/Chronos/Overview/chronos.md doc/Chronos/Overview/chronos.md
doc/Chronos/Overview/data_processing_feature_engineering.md doc/Chronos/Overview/deep_dive.rst
doc/Chronos/Overview/forecasting.md doc/Chronos/QuickStart/index.md
doc/Chronos/Overview/anomaly_detection.md
doc/Chronos/Overview/simulation.md
doc/Chronos/Overview/useful_functionalities.md
doc/Chronos/QuickStart/chronos-autotsest-quickstart.md
doc/Chronos/QuickStart/chronos-tsdataset-forecaster-quickstart.md
doc/Chronos/QuickStart/chronos-anomaly-detector.md
.. toctree:: .. toctree::
:maxdepth: 1 :maxdepth: 1