diff --git a/docs/readthedocs/source/doc/Chronos/Howto/how-to-create-forecaster.ipynb b/docs/readthedocs/source/doc/Chronos/Howto/how-to-create-forecaster.ipynb
new file mode 100644
index 00000000000..f1eee321e72
--- /dev/null
+++ b/docs/readthedocs/source/doc/Chronos/Howto/how-to-create-forecaster.ipynb
@@ -0,0 +1,206 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "[](https://colab.research.google.com/github/intel-analytics/BigDL/blob/main/docs/readthedocs/source/doc/Chronos/Howto/how-to-create-forecaster.ipynb)\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# How to create a Forecaster"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Introduction\n",
+ "\n",
+ "In Chronos, Forecaster (`bigdl.chronos.forecaster.Forecaster`) is the forecasting abstraction. It hides the complex logic of model's creation, training, scaling to cluster, tuning, optimization and inferencing while expose some APIs for users to control.\n",
+ "\n",
+ "In this guide, we will use the `TCNForecaster` and nyc_taxi datasets as examples to describe **how to create a Forecaster**."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Prepare Environments"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Before creating a forecaster, we need to install Chronos. Chronos supports deep learning backend implemented by pytorch and tensorflow and machine learning methods based on arima and prophet."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# uncomment following 1 lines for pytorch backend\n",
+ "!pip install --pre --upgrade bigdl-chronos[pytorch]\n",
+ "\n",
+ "# uncomment following 2 lines for tensorflow backend\n",
+ "# !pip install --pre --upgrade bigdl-chronos\n",
+ "# !pip install --pre --upgrade bigdl-nano[tensorflow]\n",
+ "\n",
+ "# installation trick on colab (no need to do these on your own environment)\n",
+ "!pip uninstall torchtext -y\n",
+ "exit()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Create a forecaster\n",
+ "We provide two ways to create a Forecaster.\n",
+ "\n",
+ "* Create by `Forecaster.from_tsdataset`(**Recommended if valid**)\n",
+ "* Create by `Forecaster(...)` directly"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Before creating a Forecaster, We need to know the four parameters `past_seq_len`, `future_seq_len`, `input_feature_num`, `output_feature_num`, which represent the time step and feature column, As shown below.\n",
+ "\n",
+ "![](\"../Image/forecast-RR.png\" "\\"time")
\n",
+ "\n",
+ "* **past_seq_len**: Sampled input length, represents the history time step length. (i.e. lookback)\n",
+ "* **future_seq_len**: Sampled output length, represents the output time step length.(i.e. horizon)\n",
+ "* **input_feature_num**: All feature column(s), including extra feature column(s) and target column(s).\n",
+ "* **output_feature_num**: Only target column(s).\n",
+ "\n",
+ "More Forecaster info, please refer to [Time Series Forecasting OverView](https://bigdl.readthedocs.io/en/latest/doc/Chronos/Overview/forecasting.html#time-series-forecasting-overview)\n",
+ "\n",
+ "If you want to create a traditional statistic forecaster(e.g. [ProphetForecaster](https://bigdl.readthedocs.io/en/latest/doc/PythonAPI/Chronos/forecasters.html#prophetforecaster) or [ARIMAForecaster](https://bigdl.readthedocs.io/en/latest/doc/PythonAPI/Chronos/forecasters.html#arimaforecaster)), you may refer to their API doc directly since they are relatively easy and do not have required parameters to create them."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# create a TSDataset\n",
+ "from bigdl.chronos.data.repo_dataset import get_public_dataset\n",
+ "\n",
+ "tsdataset = get_public_dataset('nyc_taxi', with_split=False)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Forecaster.from_tsdataset\n",
+ "\n",
+ "`from_tsdataset` is a classmethod, so you can call `Forecsater.from_tsdataset`, then input a `TSDataset` instance, where `TSDataset` is a built-in time series preprocessing class.\n",
+ "\n",
+ "If the `roll` or `to_torch_data_loader` method has been called by tsdataset, `past_seq_len` and `future_seq_len` do not need to be specified for from_tsdataset, otherwise both must be specified."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# uncomment following 1 lines for pytorch backend\n",
+ "from bigdl.chronos.forecaster import TCNForecaster\n",
+ "\n",
+ "# uncomment following 1 lines for tensorflow backend\n",
+ "# from bigdl.chronos.forecaster.tf import TCNForecaster\n",
+ "\n",
+ "tcn = TCNForecaster.from_tsdataset(tsdataset,\n",
+ " past_seq_len=48,\n",
+ " future_seq_len=5)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "> 📝 **Note**\n",
+ ">\n",
+ "> We recommend to use `Forecsater.from_tsdataset` if possible. While for some reasons, some forecasters (e.g. `ProphetForecaster` and `ARIMAForecaster`) does not support this API. Or maybe you want to process your data customizedly without using `TSDataset`, you may create a forecaster directly by calling `Forecaster(...)`."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Create a forecaster directly\n",
+ "You can also create forecaster directly, the parameters mentioned above still need to be specified."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "tcn = TCNForecaster(past_seq_len=48,\n",
+ " future_seq_len=5,\n",
+ " input_feature_num=2,\n",
+ " output_feature_num=2)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3.7.13 ('chronos-deps')",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.13"
+ },
+ "vscode": {
+ "interpreter": {
+ "hash": "1a7f5d41b94c9ba67b8a1438ec071a63464312bab4ac0991ee31faebf1c9c228"
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/readthedocs/source/doc/Chronos/Howto/index.rst b/docs/readthedocs/source/doc/Chronos/Howto/index.rst
index 2a1d5c16ded..931904e7ac1 100644
--- a/docs/readthedocs/source/doc/Chronos/Howto/index.rst
+++ b/docs/readthedocs/source/doc/Chronos/Howto/index.rst
@@ -4,6 +4,10 @@ How-to guides are bite-sized, executable examples where users could check when m
Forecasting
-------------------------
+* `Create a forecaster `__
+
+ In this guidance, we demonstrate **how to create a Forecaster**. Including two ways of creating a forecaster and an explanation of some important parameters.
+
* `Train forecaster on single node `__
In this guidance, **we demonstrate how to train forecasters on one node**. In the training process, forecaster will learn the pattern (like the period, scale...) in history data. Although Chronos supports training on a cluster, it's highly recommeneded to try one node first before allocating a cluster to make life easier.
@@ -17,6 +21,6 @@ Forecasting
:maxdepth: 1
:hidden:
+ how-to-create-forecaster
how_to_train_forecaster_on_one_node
-
how_to_tune_forecaster_model