diff --git a/docs/readthedocs/source/doc/Chronos/Overview/chronos.md b/docs/readthedocs/source/doc/Chronos/Overview/chronos.md
index 0eb2db34..199ba96a 100644
--- a/docs/readthedocs/source/doc/Chronos/Overview/chronos.md
+++ b/docs/readthedocs/source/doc/Chronos/Overview/chronos.md
@@ -10,6 +10,8 @@ You can use _Chronos_ to do:
 - **Anomaly Detection** (using [Anomaly Detectors](./anomaly_detection.html#anomaly-detection))
 - **Synthetic Data Generation** (using [Simulators](./simulation.html#generate-synthetic-data))
 
+Furthermore, Chronos is adapted to integrate many optimized library and best known methods(BKMs) for accuracy and performance improvement.
+
 ---
 ### **2. Install**
 
@@ -26,6 +28,12 @@ pip install bigdl-chronos[all]
 # nightly built version
 pip install --pre --upgrade bigdl-chronos[all]
 ```
+```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.
+```
 ---
 ### **3. Run**
 Various python programming environments are supported to run a _Chronos_ application.
@@ -46,6 +54,18 @@ You can directly write _Chronos_ application in a python file (e.g. script.py) a
 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**
 
@@ -61,7 +81,7 @@ Otherwise, there is no need to initialize an orca context.
 View [Orca Context](../../Orca/Overview/orca-context.md) for more details. Note that argument `init_ray_on_spark` must be `True` for _Chronos_. 
 
 ```python
-from bigdl.orca.common import init_orca_context, stop_orca_context
+from bigdl.orca import init_orca_context, stop_orca_context
 
 # run in local mode
 init_orca_context(cluster_mode="local", cores=4, init_ray_on_spark=True)
@@ -101,7 +121,6 @@ for tsdata in [tsdata_train, tsdata_val, tsdata_test]:
 
 # AutoTSEstimator initalization
 autotsest = AutoTSEstimator(model="tcn",
-                            past_seq_len=hp.randint(50, 200),
                             future_seq_len=10)
 
 # AutoTSEstimator fitting
diff --git a/docs/readthedocs/source/doc/Chronos/Overview/forecasting.md b/docs/readthedocs/source/doc/Chronos/Overview/forecasting.md
index 8e4e1736..ccf9392d 100644
--- a/docs/readthedocs/source/doc/Chronos/Overview/forecasting.md
+++ b/docs/readthedocs/source/doc/Chronos/Overview/forecasting.md
@@ -7,21 +7,38 @@ There're three ways to do forecasting:
 - Use [**auto forecasting models**](#use-auto-forecasting-model) with auto hyperparameter optimization.
 - Use [**standalone forecasters**](#use-standalone-forecaster-pipeline).
 
+#### **0. Supported Time Series Forecasting Model**
+
+- `Model`: Model name.
+- `Style`: Forecasting model style. Detailed information will be stated in [this section](#time-series-forecasting-concepts).
+- `Multi-Variate`: Predict more than one variable at the same time?
+- `Multi-Step`: Predict more than one data point in the future?
+- `Exogenous Variables`: Take other variables(you don't need to predict) into consideration?
+- `Distributed`: Scale the model to a cluster and take data from distributed file system?
+- `ONNX`: Export and use `OnnxRuntime` to do the inference.
+- `Quantization`: Export and use quantized int8 model to do the inference.
+- `Auto Models`: AutoModel API support.
+- `AutoTS`: AutoTS API support.
+- `Backend`: The DL framework we use to implement this model.
+
 <span id="supported_forecasting_model"></span>
 
-| Model   | Style | Multi-Variate | Multi-Step | Distributed\* | Auto Models | AutoTS | Backend |
-| ----------------- | ----- | ------------- | ---------- | ----------- | ----------- | ----------- | ----------- |
-| LSTM    | RR    | ✅             | ❌          | ✅           | ✅          | ✅         | pytorch  |
-| Seq2Seq     | RR    | ✅             | ✅          | ✅           | ✅          | ✅         | pytorch  |
-| TCN | RR    | ✅             | ✅          | ✅           | ✅          | ✅         | pytorch  |
-| MTNet   | RR    | ✅             | ❌         | ✅           | ❌          | ✳️\*\*\*        | tensorflow |
-| TCMF    | TS    | ✅             | ✅          | ✳️\*\*           | ❌          | ❌         | pytorch  |
-| Prophet | TS    | ❌             | ✅          | ❌           | ✅          | ❌         | prophet  |
-| ARIMA   | TS    | ❌             | ✅          | ❌           | ✅          | ❌         | pmdarima |
+| Model   | Style | Multi-Variate | Multi-Step | Exogenous Variables | Distributed | ONNX | Quantization | Auto Models | AutoTS | Backend |
+| ----------------- | ----- | ------------- | ---------- | ------- | ----------- | ----------- | ----------- | ----------- | ----------- | ----------- |
+| LSTM    | RR    | ✅             | ❌      | ✅    | ✅   | ✅           | ✅        | ✅          | ✅         | pytorch  |
+| Seq2Seq     | RR    | ✅             | ✅     | ✅     | ✅     | ✅           | ❌      | ✅          | ✅         | pytorch  |
+| TCN | RR    | ✅             | ✅     | ✅     | ✅     | ✅           | ✅      | ✅          | ✅         | pytorch  |
+| NBeats | RR    | ❌             | ✅     | ❌     | ✅     | ✅           | ✅      | ❌          | ❌         | pytorch  |
+| MTNet   | RR    | ✅             | ❌    | ✅     | ✅     | ❌          | ❌         | ❌          | ✳️\*\*        | tensorflow |
+| TCMF    | TS    | ✅             | ✅    | ✅      | ✳️\*     | ❌          | ❌         | ❌          | ❌         | pytorch  |
+| Prophet | TS    | ❌             | ✅    | ❌      | ❌        | ❌          | ❌      | ✅          | ❌         | prophet  |
+| ARIMA   | TS    | ❌             | ✅    | ❌      | ❌         | ❌          | ❌     | ✅          | ❌         | pmdarima |
+| Customized\*\*\* | RR | Customized | Customized | Customized | ❌ |✅|❌|❌|✅|pytorch
+
+\* TCMF only partially supports distributed training.<br>
+\*\*  Auto tuning of MTNet is only supported in our deprecated AutoTS API.<br>
+\*\*\* Customized model is only supported in `AutoTSEstimator` with pytorch as backend.
 
-\* Distributed training/inferencing is only supported by standalone forecasters.<br>
-\*\* TCMF only partially supports distributed training.<br>
-\*\*\*  Auto tuning of MTNet is only supported in our deprecated AutoTS API.<br>
 
 
 #### **1. Time Series Forecasting Concepts**
@@ -203,14 +220,19 @@ ProphetForecaster wraps the Prophet model ([site](https://github.com/facebook/pr
 
 View Stock Prediction [notebook](https://github.com/intel-analytics/BigDL/blob/branch-2.0/python/chronos/use-case/fsi/stock_prediction_prophet.ipynb) and [ProphetForecaster API Doc](../../PythonAPI/Chronos/forecasters.html#prophetforecaster) for more details.
 
+<span id="NBeatsForecaster"></span>
+###### **3.8 NBeatsForecaster**
+
+Neural basis expansion analysis for interpretable time series forecasting ([N-BEATS](https://arxiv.org/abs/1905.10437)) is a deep neural architecture based on backward and forward residual links and a very deep stack of fully-connected layers. Nbeats can solve univariate time series point forecasting problems, being interpretable, and fast to train.
+
 #### **4. Use Auto forecasting model**
 Auto forecasting models are designed to be used exactly the same as Forecasters. The only difference is that you can set hp search function to the hyperparameters and the `.fit()` method will search the best hyperparameter setting.
 ```python
 # set hyperparameters in hp search function, loss, metric...
-f = Forecaster(...)
+auto_model = AutoModel(...)
 # input data, batch size, epoch...
-f.fit(...)
+auto_model.fit(...)
 # input test data x, batch size...
-f.predict(...)
+auto_model.predict(...)
 ```
 The input data can be easily get from `TSDataset`. Users can refer to detailed [API doc](../../PythonAPI/Chronos/automodels.html).
\ No newline at end of file
diff --git a/docs/readthedocs/source/doc/Chronos/Overview/useful_functionalities.md b/docs/readthedocs/source/doc/Chronos/Overview/useful_functionalities.md
index 96a898d7..57d6a050 100644
--- a/docs/readthedocs/source/doc/Chronos/Overview/useful_functionalities.md
+++ b/docs/readthedocs/source/doc/Chronos/Overview/useful_functionalities.md
@@ -90,3 +90,29 @@ f = Forecaster(..., distributed=True)
 f.fit(tsdata_xshards, ...)
 f.predict(test_tsdata_xshards, ...)
 ```
+#### **5. Quantization**
+Quantization refers to processes that enable lower precision inference. In Chronos, post-training quantization is supported relied on [Intel® Neural Compressor](https://intel.github.io/neural-compressor/README.html).
+```python
+# init
+f = Forecaster(...)
+
+# train the forecaster
+f.fit(train_data, ...)
+
+# quantize the forecaster
+f.quantize(train_data, ...)
+
+# predict with int8 model with better inference throughput
+f.predict(test_data, quantize=True)
+
+# predict with fp32
+f.predict(test_data, quantize=False)
+
+# save
+f.save(checkpoint_file="fp32.model"
+       quantize_checkpoint_file="int8.model")
+
+# load
+f.load(checkpoint_file="fp32.model"
+       quantize_checkpoint_file="int8.model")
+```