<commit_before># Chronos User Guide

### **1. Overview**
_Chronos_ is an application framework for building large-scale time series analysis applications.

You can use _Chronos_ to do:

- **Data pre/post-processing and feature generation** (using [TSDataset](./data_processing_feature_engineering.html))
- **Time Series Forecasting** (using [Standalone Forecasters](./forecasting.html#use-standalone-forecaster-pipeline), [Auto Models](./forecasting.html#use-auto-forecasting-model) (with HPO) or [AutoTS](./forecasting.html#use-autots-pipeline) (full AutoML enabled pipelines))
- **Anomaly Detection** (using [Anomaly Detectors](./anomaly_detection.html#anomaly-detection))
- **Synthetic Data Generation** (using [Simulators](./simulation.html#generate-synthetic-data))
- **Speed up or tune your customized time-series model** (using TSTrainer and [AutoTS](./forecasting.html#use-autots-pipeline))

---
### **2. Install**

Install `bigdl-chronos` from PyPI. We recommened to install with a conda virtual environment. To install Conda, please refer to https://docs.conda.io/en/latest/miniconda.html#.
```bash
conda create -n my_env python=3.7 setuptools=58.0.4
conda activate my_env
pip install bigdl-chronos
```
You may also install `bigdl-chronos` with target `[all]` to install the additional dependencies for _Chronos_. This will enable distributed tuning with AutoTS.
```bash
# stable version
pip install bigdl-chronos[all]
# nightly built version
pip install --pre --upgrade bigdl-chronos[all]
# set env variables for your conda environment
source bigdl-nano-init
```
Some dependencies are optional and not included in `bigdl-chronos[all]`. You may install them when you want to use corresponding functionalities. This includes:
```bash
pip install tsfresh==0.17.0
pip install bigdl-nano[tensorflow]
pip install pmdarima==1.8.2
pip install prophet==1.0.1
pip install neural-compressor==1.8.1
pip install pyarrow==6.0.1
```
```eval_rst
.. note:: 
    **Supported OS**:

     Chronos is thoroughly tested on Ubuntu (16.04/18.04/20.04). If you are a Windows user, the most convenient way to use Chronos on a windows laptop might be using WSL2, you may refer to https://docs.microsoft.com/en-us/windows/wsl/setup/environment or just install a ubuntu virtual machine.
```
```eval_rst
.. note:: 
    **Supported Python Version**:

     Chronos only supports Python 3.7.2 ~ latest 3.7.x.
```
---
### **3. Run**
Various python programming environments are supported to run a _Chronos_ application.

#### **3.1 Jupyter Notebook**

You can start the Jupyter notebook as you normally do using the following command and run  _Chronos_ application directly in a Jupyter notebook:

```bash
jupyter notebook --notebook-dir=./ --ip=* --no-browser
```

#### **3.2 Python Script**

You can directly write _Chronos_ application in a python file (e.g. script.py) and run in the command line as a normal Python program:

```bash
python script.py
```

```eval_rst
.. note:: 
    **Optimization on Intel® Hardware**:
    
     Chronos integrated many optimized library and best known methods(BKMs), users can have best performance to add ``bigdl-nano-init`` before their scripts. 
     
     ``bigdl-nano-init python script.py``

     Currently, this function is under active development and we encourage our users to add ``bigdl-nano-init`` for forecaster's training.
     
```

---
### **4. Get Started**

#### **4.1 Initialization**
_Chronos_ uses [Orca](../../Orca/Overview/orca.md) to enable distributed training and AutoML capabilities. Initialize orca as below when you want to:<commit_msg>Chronos: Correct typo for Chronos User Guide (#5166)<commit_after># Chronos User Guide

### **1. Overview**
_Chronos_ is an application framework for building large-scale time series analysis applications.

You can use _Chronos_ to do:

- **Data pre/post-processing and feature generation** (using [TSDataset](./data_processing_feature_engineering.html))
- **Time Series Forecasting** (using [Standalone Forecasters](./forecasting.html#use-standalone-forecaster-pipeline), [Auto Models](./forecasting.html#use-auto-forecasting-model) (with HPO) or [AutoTS](./forecasting.html#use-autots-pipeline) (full AutoML enabled pipelines))
- **Anomaly Detection** (using [Anomaly Detectors](./anomaly_detection.html#anomaly-detection))
- **Synthetic Data Generation** (using [Simulators](./simulation.html#generate-synthetic-data))
- **Speed up or tune your customized time-series model** (using TSTrainer and [AutoTS](./forecasting.html#use-autots-pipeline))

---
### **2. Install**

Install `bigdl-chronos` from PyPI. We recommend to install with a conda virtual environment. To install Conda, please refer to https://docs.conda.io/en/latest/miniconda.html#.
```bash
conda create -n my_env python=3.7 setuptools=58.0.4
conda activate my_env
pip install bigdl-chronos
```
You may also install `bigdl-chronos` with target `[all]` to install the additional dependencies for _Chronos_. This will enable distributed tuning with AutoTS.
```bash
# stable version
pip install bigdl-chronos[all]
# nightly built version
pip install --pre --upgrade bigdl-chronos[all]
# set env variables for your conda environment
source bigdl-nano-init
```
Some dependencies are optional and not included in `bigdl-chronos[all]`. You may install them when you want to use corresponding functionalities. This includes:
```bash
pip install tsfresh==0.17.0
pip install bigdl-nano[tensorflow]
pip install pmdarima==1.8.2
pip install prophet==1.0.1
pip install neural-compressor==1.8.1
pip install pyarrow==6.0.1
```
```eval_rst
.. note:: 
    **Supported OS**:

     Chronos is thoroughly tested on Ubuntu (16.04/18.04/20.04). If you are a Windows user, the most convenient way to use Chronos on a windows laptop might be using WSL2, you may refer to https://docs.microsoft.com/en-us/windows/wsl/setup/environment or just install a ubuntu virtual machine.
```
```eval_rst
.. note:: 
    **Supported Python Version**:

     Chronos only supports Python 3.7.2 ~ latest 3.7.x.
```
---
### **3. Run**
Various Python programming environments are supported to run a _Chronos_ application.

#### **3.1 Jupyter Notebook**

You can start the Jupyter notebook as you normally do using the following command and run  _Chronos_ application directly in a Jupyter notebook:

```bash
jupyter notebook --notebook-dir=./ --ip=* --no-browser
```

#### **3.2 Python Script**

You can directly write _Chronos_ application in a Python file (e.g. script.py) and run in the command line as a normal Python program:

```bash
python script.py
```

```eval_rst
.. note:: 
    **Optimization on Intel® Hardware**:
    
     Chronos integrated many optimized libraries and best known methods (BKMs), users can have best performance to add ``bigdl-nano-init`` before their scripts. 
     
     ``bigdl-nano-init python script.py``

     Currently, this function is under active development and we encourage our users to add ``bigdl-nano-init`` for forecaster's training.
     
```

---
### **4. Get Started**

#### **4.1 Initialization**
_Chronos_ uses [Orca](../../Orca/Overview/orca.md) to enable distributed training and AutoML capabilities. Initialize orca as below when you want to:
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