Dev tutorials update

README.md

@@ -17,23 +17,19 @@ This project
 - requires PyStan as a system dependency. PyStan is licensed under [GPLv3](https://www.gnu.org/licenses/gpl-3.0.html), which is a free, copyleft license for software.
 
 
-# Orbit: A Python Package for Bayesian Forecasting 
+# Orbit: A Python Package for Bayesian Forecasting
 
-Orbit is a Python package for Bayesian forecasting models developed under object-oriented design. It provides a
-familiar and intuitive initialize-fit-predict interface for working with
-time series tasks, while utilizing probabilistic modeling api under
-the hood.
+Orbit is a Python package for Bayesian time series forecasting and inference. It provides a
+familiar and intuitive initialize-fit-predict interface for time series tasks, while utilizing probabilistic programing languages under the hood.
 
-The initial release supports concrete implementation for the following
+Currently, it supports concrete implementations for the following
 models:
 
+-  Exponential Smoothing (ETS)
 -  Damped Local Trend (DLT)
 -  Local Global Trend (LGT)
 
-Both models, which are variants of exponential smoothing, support
-seasonality and exogenous (time-independent) features.
-
-The initial release also supports the following sampling methods for
+It also supports the following sampling methods for
 model estimation:
 
 -  Markov-Chain Monte Carlo (MCMC) as a full sampling method
@@ -100,7 +96,7 @@ plot_predicted_data(
 # References
 ##  Documentation
 
-- [Orbit API Documentation and Examples](https://uber.github.io/orbit/>)
+- [Orbit API Documentation and Examples](https://uber.github.io/orbit/)
 
 ##  Papers
 
@@ -111,9 +107,9 @@ plot_predicted_data(
 
 ## Related projects
 
-- [Pyro](https://github.com/pyro-ppl/pyro>)
-- [Stan](https://github.com/stan-dev/stan>)
-- [Rlgt](https://cran.r-project.org/web/packages/Rlgt/index.html>)
+- [Pyro](https://github.com/pyro-ppl/pyro)
+- [Stan](https://github.com/stan-dev/stan)
+- [Rlgt](https://cran.r-project.org/web/packages/Rlgt/index.html)
 
 # Citation
 
@@ -124,13 +120,13 @@ To cite Orbit in publications, refer to the following whitepaper:
 Bibtex:
 ```
 @misc{
-    ng2020orbit, 
-    title={Orbit: Probabilistic Forecast with Exponential Smoothing}, 
-    author={Edwin Ng, 
-        Zhishi Wang, 
-        Huigang Chen, 
-        Steve Yang, 
-        Slawek Smyl}, 
+    ng2020orbit,
+    title={Orbit: Probabilistic Forecast with Exponential Smoothing},
+    author={Edwin Ng,
+        Zhishi Wang,
+        Huigang Chen,
+        Steve Yang,
+        Slawek Smyl},
     year={2020}, eprint={2004.08492}, archivePrefix={arXiv}, primaryClass={stat.CO}
 }
 ```

docs/about.rst

@@ -0,0 +1,57 @@
+About Orbit
+============
+
+Orbit is a Python package for Bayesian time series modeling and inference. It provides a
+familiar and intuitive initialize-fit-predict interface for working with
+time series tasks, while utilizing probabilistic programing languages under
+the hood.
+
+Currently, it supports the following models:
+
+-  Exponential Smoothing (ETS)
+-  Local Global Trend (LGT)
+-  Damped Local Trend (DLT)
+
+It also supports the following sampling methods for
+model estimation:
+
+-  Markov-Chain Monte Carlo (MCMC) as a full sampling method
+-  Maximum a Posteriori (MAP) as a point estimate method
+-  Variational Inference (VI) as a hybrid-sampling method on approximate
+   distribution
+
+Quick Example
+-------------
+
+Orbit APIs follow a Scikit-learn stype API design, with a user-friendly interface. After instantiating a model
+object, one can use .fit and .predict for model training and prediction. Below is a quick illustration using the DLT model.
+
+.. code:: python
+
+    from orbit.models.dlt import DLTFull
+
+    dlt = DLTFull(
+        response_col='claims',
+        date_col='week',
+        regressor_col=['trend.unemploy', 'trend.filling', 'trend.job'],
+        seasonality=52,
+    )
+
+    dlt.fit(df=train_df)
+
+    predicted_df = dlt.predict(df=test_df)
+
+
+Citation
+--------
+
+To cite Orbit in publications, refer to the following whitepaper:
+
+`Orbit: Probabilistic Forecast with Exponential Smoothing <https://arxiv.org/abs/2004.08492>`__
+
+Bibtex:
+
+  @misc{ng2020orbit, title={Orbit: Probabilistic Forecast with Exponential Smoothing}, author={Edwin Ng, Zhishi Wang, Huigang Chen, Steve Yang, Slawek Smyl}, year={2020}, eprint={2004.08492}, archivePrefix={arXiv}, primaryClass={stat.CO}}
+
+
+

docs/conf.py

@@ -21,7 +21,7 @@
 
 project = 'orbit'
 copyright = '2020, Uber Technologies, Inc'
-author = 'Edwin Ng, Steve Yang, Huigang Chen, Zhishi Wang'
+author = 'Edwin Ng, Steve Yang, Zhishi Wang, Yifeng Wu, Jing Pan'
 
 # The short X.Y version.
 version = orbit.__version__
@@ -82,7 +82,7 @@
 #
 html_theme_options = {
     # 'logo_only': False,
-    'navigation_depth': 3,
+    'navigation_depth': 1,
 }
 
 # Add any paths that contain custom static files (such as style sheets) here,

docs/index.rst

@@ -9,10 +9,38 @@ Welcome to Orbit's Documentation!
 
 .. toctree::
    :maxdepth: 1
-   :caption: Orbit Core
+   :caption: Basic
+   :name: basic
 
+   about
    installation
-   tutorials
+   tutorials/quick_start
+   tutorials/model_estimations
+   tutorials/pyro_basic
+
+
+.. toctree::
+   :maxdepth: 1
+   :caption: Exponential Smoothing Models
+   :name: exponential-smoothing-models
+
+   tutorials/dlt
+   tutorials/lgt
+   tutorials/regression
+
+.. toctree::
+   :maxdepth: 1
+   :caption: Validation
+
+   tutorials/decompose_prediction
+   tutorials/model_diagnostics
+   tutorials/backtest
+
+.. toctree::
+   :maxdepth: 1
+   :caption: Other Utilities
+
+   tutorials/utilities_simulation
    modules
 
 Indices and tables