Fix readme

README.md

@@ -1,4 +1,7 @@
-# Luminaire: A hands-off Anomaly Detection Library
+<!-- Logo & Title -->
+<p align="center">
+<img width=450 src="https://github.com/zillow/luminaire/blob/master/docs/assets/luminaire_logo_blue.svg" alt="Luminaire"></p>
+<h3 align="center">A hands-off Anomaly Detection Library</h3>
 
 <!-- Badges Start-->
 [![PyPI version](https://badge.fury.io/py/luminaire.svg)](https://badge.fury.io/py/luminaire)
@@ -9,8 +12,7 @@
 [![docs](https://github.com/zillow/luminaire/workflows/Luminaire%20Docs/badge.svg)](https://github.com/zillow/luminaire/actions?query=workflow%3A%22Luminaire+Docs%22)
 <!-- Badges End -->
 
-![Luminaire Logo](docs/assets/luminaire_logo.png)
-
+---
 **Table of contents**
 
 - [What is Luminaire](#what-is-luminaire)
@@ -24,10 +26,7 @@
 
 ## What is Luminaire
 
-[Luminaire](https://zillow.github.io/luminaire/introduction.html) is a python package 
-that provides ML-driven solutions for monitoring time series data. Luminaire provides several anomaly detection and 
-forecasting capabilities that incorporate correlational and seasonal patterns as well as uncontrollable variations in 
-the data over time.
+[Luminaire](https://zillow.github.io/luminaire/) is a python package that provides ML-driven solutions for monitoring time series data. Luminaire provides several anomaly detection and forecasting capabilities that incorporate correlational and seasonal patterns as well as uncontrollable variations in the data over time.
 
 ## Quick Start
 
@@ -42,8 +41,7 @@ Import ``luminaire`` module in python
 import luminaire
 ```
 
-Check out [Luminaire documentation](https://zillow.github.io/luminaire/introduction.html) 
-for detailed description of methods and usage.
+Check out [Luminaire documentation](https://zillow.github.io/luminaire) for detailed description of methods and usage.
 
 
 ## Time Series Outlier Detection Workflow
@@ -53,40 +51,29 @@ Luminaire outlier detection workflow can be divided into 3 major components:
 
 ### Data Preprocessing and Profiling Component
 
-This component can be called to prepare a time series prior to training an anomaly detection model on it. This step applies
-a number of methods that make anomaly detection more accurate and reliable, including missing data imputation, identifying and removing recent outliers from training data, necessary mathematical transformations, and data truncation based on recent change points. It also generates profiling 
-information (historical change points, trend changes, etc.) that are considered in the training process. 
+This component can be called to prepare a time series prior to training an anomaly detection model on it. This step applies a number of methods that make anomaly detection more accurate and reliable, including missing data imputation, identifying and removing recent outliers from training data, necessary mathematical transformations, and data truncation based on recent change points. It also generates profiling information (historical change points, trend changes, etc.) that are considered in the training process. 
 
 Profiling information for time series data can be used to monitor data drift and irregular long-term swings.
 
 ### Modeling Component
 
-This component performs time series model training based on the user-specified configuration OR optimized configuration 
-(see Luminaire hyperparameter optimization). Luminaire model training is integrated with different structural time series
-models as well as filtering based models. See [Luminaire outlier detection](https://zillow.github.io/luminaire/basic_usage_tutorial/outlier_batch.html)
-for more information.
+This component performs time series model training based on the user-specified configuration OR optimized configuration (see Luminaire hyperparameter optimization). Luminaire model training is integrated with different structural time series models as well as filtering based models. See [Luminaire outlier detection](https://zillow.github.io/luminaire/tutorial/outlier_batch.html) for more information.
 
 The Luminaire modeling step can be called after the data preprocessing and profiling step to perform necessary data preparation before training.
 
 ### Configuration Optimization Component
 
 Luminaire's integration with configuration optimization enables a hands-off anomaly detection process where
-the user needs to provide very minimal configuration for monitoring any type of time series data. This step can be combined with
-the preprocessing and modeling for any auto-configured anomaly detection use case. See [fully automatic outlier detection](https://zillow.github.io/luminaire/basic_usage_tutorial/optimization.html#fully-automatic-outlier-detection)
-for a detailed walkthrough. 
-
+the user needs to provide very minimal configuration for monitoring any type of time series data. This step can be combined with the preprocessing and modeling for any auto-configured anomaly detection use case. See [fully automatic outlier detection](https://zillow.github.io/luminaire/tutorial/optimization.html#fully-automatic-outlier-detection) for a detailed walkthrough. 
 
 ## Anomaly Detection for High Frequency Time Series
 
-Luminaire can also monitor a set of data points over windows of time instead of tracking individual data points. This approach is well-suited
-for streaming use cases where sustained fluctuations are of greater concern than individual fluctuations. See [anomaly detection for streaming data](https://zillow.github.io/luminaire/basic_usage_tutorial/streaming.html) for detailed information.
-
+Luminaire can also monitor a set of data points over windows of time instead of tracking individual data points. This approach is well-suited for streaming use cases where sustained fluctuations are of greater concern than individual fluctuations. See [anomaly detection for streaming data](https://zillow.github.io/luminaire/tutorial/streaming.html) for detailed information.
 
 ## Contributing
 
 Want to help improve Luminaire? Check out our [contributing documentation](CONTRIBUTING.rst).
 
-
 ## Acknowledgements
 
 This project has leveraged methods described in the following scientific publications:
@@ -96,6 +83,4 @@ This project has leveraged methods described in the following scientific publica
 
 ## Development Team
 
-Luminaire is developed and maintained by [Sayan Chakraborty](https://github.com/sayanchk), [Smit Shah](https://github.com/shahsmit14), 
-[Kiumars Soltani](https://github.com/kiumarss), [Luyao Yang]( https://github.com/snazzyfox), [Anna Swigart](https://github.com/annaswigart), 
-[Kyle Buckingham](https://github.com/kylebuckingham) and many other contributors from the Zillow Group A.I. team.
+Luminaire is developed and maintained by [Sayan Chakraborty](https://github.com/sayanchk), [Smit Shah](https://github.com/shahsmit14), [Kiumars Soltani](https://github.com/kiumarss), [Luyao Yang]( https://github.com/snazzyfox), [Anna Swigart](https://github.com/annaswigart), [Kyle Buckingham](https://github.com/kylebuckingham) and many other contributors from the Zillow Group A.I. team.