Chronos: Refactor document structure (#3839)
* add strcture change * add change in index * add change to index.rst * revice the document * add icons * revise on the pages * add readme update * remove ood information * change title
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docs/readthedocs/source/doc/Chronos/Overview/deep_dive.rst
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docs/readthedocs/source/doc/Chronos/Overview/deep_dive.rst
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Chronos Deep Dive
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=========
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* `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.
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* `Time Series Forecasting <forecasting.md>`__ introduces how to build a time series forecasting application.
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* `Time Series Anomaly Detection <anomaly_detection.md>`__ introduces how to build a anomaly detection application.
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* `Generate Synthetic Sequential Data <simulation.md>`__ introduces how to build a series data generation application.
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* `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.
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.. toctree::
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:maxdepth: 1
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:hidden:
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data_processing_feature_engineering.md
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forecasting.md
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anomaly_detection.md
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simulation.md
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useful_functionalities.md
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# Anomaly Detector Quickstart
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# Detect Anomaly Point in Real Time Traffic Data
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# AutoTSEstimator Quickstart
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# Tune a Forecasting Task Automatically
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# TSDataset and Forecaster Quickstarts
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# Predict Number of Taxi Passengers with Chronos Forecaster
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docs/readthedocs/source/doc/Chronos/QuickStart/index.md
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docs/readthedocs/source/doc/Chronos/QuickStart/index.md
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# Chronos Tutorial
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- [**Predict Number of Taxi Passengers with Chronos Forecaster**](./chronos-tsdataset-forecaster-quickstart.html)
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[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) [View source on GitHub](https://github.com/intel-analytics/BigDL/blob/branch-2.0/python/chronos/colab-notebook/chronos_nyc_taxi_tsdataset_forecaster.ipynb)
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In this guide we will demonstrate how to use _Chronos TSDataset_ and _Chronos Forecaster_ for time series processing and predict number of taxi passengers.
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- [**Tune a Forecasting Task Automatically**](./chronos-autotsest-quickstart.html)
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[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) [View source on GitHub](https://github.com/intel-analytics/BigDL/blob/branch-2.0/python/chronos/colab-notebook/chronos_autots_nyc_taxi.ipynb)
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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.
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- [**Detect Anomaly Point in Real Time Traffic Data**](./chronos-anomaly-detector.html)
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[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) [View source on GitHub](https://github.com/intel-analytics/BigDL/blob/branch-2.0/python/chronos/colab-notebook/chronos_minn_traffic_anomaly_detector.ipynb)
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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
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:caption: Chronos Overview
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doc/Chronos/Overview/chronos.md
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doc/Chronos/Overview/chronos.md
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doc/Chronos/Overview/data_processing_feature_engineering.md
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doc/Chronos/Overview/deep_dive.rst
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doc/Chronos/Overview/forecasting.md
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doc/Chronos/QuickStart/index.md
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doc/Chronos/Overview/anomaly_detection.md
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doc/Chronos/Overview/simulation.md
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doc/Chronos/Overview/useful_functionalities.md
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doc/Chronos/QuickStart/chronos-autotsest-quickstart.md
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doc/Chronos/QuickStart/chronos-tsdataset-forecaster-quickstart.md
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doc/Chronos/QuickStart/chronos-anomaly-detector.md
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.. toctree::
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.. toctree::
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:maxdepth: 1
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:maxdepth: 1
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