Update README and docs to reflect general applicability of TimeSeriesClustering. Update references.

Author: holgerteichgraeber

README.md

@@ -61,7 +61,7 @@ If you find TimeSeriesClustering useful in your work, we kindly request that you
 
 ## Quick Start Guide
 
-This quick start guide introduces the main concepts of using TimeSeriesClustering. For more detail on the different functionalities that TimeSeriesClustering provides, please refer to the subsequent chapters of the documentation or the examples in the [examples](https://github.com/holgerteichgraeber/TimeSeriesClustering.jl/tree/master/examples) folder, specifically [workflow_introduction.jl](https://github.com/holgerteichgraeber/TimeSeriesClustering.jl/blob/master/examples/workflow_introduction.jl).
+This quick start guide introduces the main concepts of using TimeSeriesClustering. The examples are taken from problems in the domain of scenario reduction for energy systems optimization. For more detail on the different functionalities that TimeSeriesClustering provides, please refer to the subsequent chapters of the documentation or the examples in the [examples](https://github.com/holgerteichgraeber/TimeSeriesClustering.jl/tree/master/examples) folder, specifically [workflow_introduction.jl](https://github.com/holgerteichgraeber/TimeSeriesClustering.jl/blob/master/examples/workflow_introduction.jl).
 
 Generally, the workflow consists of three steps:
 - load data

docs/src/index.md

@@ -1,22 +1,30 @@
-![TimeSeriesClustering logo](assets/clust_for_opt_text.svg)
+![TimeSeriesClustering](docs/src/assets/clust_for_opt_text.svg)
 ===
 [![](https://img.shields.io/badge/docs-stable-blue.svg)](https://holgerteichgraeber.github.io/TimeSeriesClustering.jl/stable)
 [![](https://img.shields.io/badge/docs-dev-blue.svg)](https://holgerteichgraeber.github.io/TimeSeriesClustering.jl/dev)
+[![License](http://img.shields.io/badge/license-MIT-brightgreen.svg?style=flat)](LICENSE)
 [![Build Status](https://travis-ci.com/holgerteichgraeber/TimeSeriesClustering.jl.svg?token=HRFemjSxM1NBCsbHGNDG&branch=master)](https://travis-ci.com/holgerteichgraeber/TimeSeriesClustering.jl)
 [![codecov](https://codecov.io/gh/holgerteichgraeber/TimeSeriesClustering.jl/branch/master/graph/badge.svg)](https://codecov.io/gh/holgerteichgraeber/TimeSeriesClustering.jl)
 
-[TimeSeriesClustering](https://github.com/holgerteichgraeber/TimeSeriesClustering.jl) is a [julia](https://www.juliaopt.com) implementation of unsupervised machine learning methods for finding representative periods for energy systems optimization problems.
-By reducing the number of time steps used in the optimization model, using representative periods leads to significant reductions in computational complexity.
+
+[TimeSeriesClustering](https://github.com/holgerteichgraeber/TimeSeriesClustering.jl) is a [Julia](https://www.juliaopt.com) implementation of unsupervised learning methods for time series datasets. It provides functionality for clustering and aggregating, detecting motifs, and quantifying similarity between time series datasets.
+The software provides a type system for temporal data, and provides an implementation of the most commonly used clustering methods and extreme value selection methods for temporal data.
+It provides simple integration of multi-dimensional time-series data (e.g. multiple attributes such as wind availability, solar availability, and electricity demand) in a single aggregation process.
+The software is applicable to general time series datasets and lends itself well to a multitude of application areas within the field of time series data mining.
+
+The TimeSeriesClustering package was originally developed to perform time series aggregation for energy systems optimization problems. By reducing the number of time steps used in the optimization model, using representative periods leads to significant reductions in computational complexity of these problems.
+The packages was previously known as `ClustForOpt.jl`.
 
 The package has three main purposes:
-1) Provide a simple process of finding representative periods for time-series input data, with implementations of the most commonly used clustering methods and extreme value selection methods.
-2) Provide an interface between representative period data and optimization problem by having representative period data stored in a generalized type system.
-3) Provide a generalized import feature for time series, where variable names, attributes, and node names are automatically stored and can then be used in the definition of sets of the optimization problem later.
+1) Provide a simple process of finding representative periods (reducing the number of observations) for time-series input data, with implementations of the most commonly used clustering methods and extreme value selection methods.
+2) Provide an interface between representative period data and application (e.g. optimization problem) by having representative period data stored in a generalized type system.
+3) Provide a generalized import feature for time series, where variable names, attributes, and node names are automatically stored and can then be used later when the reduced time series is used in the application at hand (e.g. in the definition of sets of the optimization problem).
 
-An example energy systems optimization problem that uses TimeSeriesClustering for its input data is the package [CapacityExpansion](https://github.com/YoungFaithful/CapacityExpansion.jl), which implements a scalable generation and transmission capacity expansion problem.
+In the domain of energy systems optimization, an example problem that uses TimeSeriesClustering for its input data is the package [CapacityExpansion](https://github.com/YoungFaithful/CapacityExpansion.jl), which implements a scalable generation and transmission capacity expansion problem.
 
 The TimeSeriesClustering package follows the clustering framework presented in [Teichgraeber and Brandt, 2019](https://doi.org/10.1016/j.apenergy.2019.02.012).
-The package is actively developed, and new features are continuously added. For a reproducible version of the methods and data of the original paper by [Teichgraeber and Brandt, 2019](https://doi.org/10.1016/j.apenergy.2019.02.012), please refer to [v0.1](https://github.com/holgerteichgraeber/TimeSeriesClustering.jl/tree/v0.1).
+The package is actively developed, and new features are continuously added.
+For a reproducible version of the methods and data of the original paper by [Teichgraeber and Brandt, 2019](https://doi.org/10.1016/j.apenergy.2019.02.012), please refer to [v0.1](https://github.com/holgerteichgraeber/TimeSeriesClustering.jl/tree/v0.1) (including shape based methods such as `k-shape` and `dynamic time warping barycenter averaging`).
 
 This package is developed by Holger Teichgraeber [@holgerteichgraeber](https://github.com/holgerteichgraeber) and Elias Kuepper [@YoungFaithful](https://github.com/youngfaithful).
 

docs/src/quickstart.md

@@ -1,7 +1,7 @@
 Quick Start Guide
 =================
 
-This quick start guide introduces the main concepts of using TimeSeriesClustering. For more detail on the different functionalities that TimeSeriesClustering provides, please refer to the subsequent chapters of the documentation or the examples in the [examples](https://github.com/holgerteichgraeber/TimeSeriesClustering.jl/tree/master/examples) folder, specifically [workflow_introduction.jl](https://github.com/holgerteichgraeber/TimeSeriesClustering.jl/blob/master/examples/workflow_introduction.jl).
+This quick start guide introduces the main concepts of using TimeSeriesClustering. The examples are taken from problems in the domain of scenario reduction for energy systems optimization. For more detail on the different functionalities that TimeSeriesClustering provides, please refer to the subsequent chapters of the documentation or the examples in the [examples](https://github.com/holgerteichgraeber/TimeSeriesClustering.jl/tree/master/examples) folder, specifically [workflow_introduction.jl](https://github.com/holgerteichgraeber/TimeSeriesClustering.jl/blob/master/examples/workflow_introduction.jl).
 
 Generally, the workflow consists of three steps:
 - load data

paper/paper.bib

@@ -12,11 +12,11 @@ @article{Teichgraeber:2019
 }
 
 
-@article{Lindenmeyer:2019,
+@article{Lindenmeyer:2020,
 author = {Lindenmeyer, Constantin and Teichgraeber, Holger and Baumgaertner, Nils and Kotzur, Leander and Robinius, Martin and Bardow, Andre and Brandt, Adam R},
 journal = {Energy},
 title = {{Extreme events as part of representative periods for the optimization of residential energy supply systems}},
-year = {2019}
+year = {2020, Manuscript in preparation}
 }
 
 @article{Novikov:2019,

paper/paper.md

@@ -20,22 +20,22 @@ authors:
 affiliations:
  - name: Department of Energy Resources Engineering, Stanford University
    index: 1
-date: 18 August 2019
+date: 2 September 2019
 bibliography: paper.bib
 ---
 
 # Summary
 
 ``TimeSeriesClustering`` is a Julia implementation of unsupervised learning methods for time series datasets. It provides functionality for clustering and aggregating, detecting motifs, and quantifying similarity between time series datasets.
 The software provides a type system for temporal data, and provides an implementation of the most commonly used clustering methods and extreme value selection methods for temporal data.
-``TimeSeriesClustering`` provides simple integration of multi-dimensional time-series data (e.g. multiple attributes such as wind availability, solar availability, and electricity demand) in a single aggregation process.
+``TimeSeriesClustering`` provides simple integration of multi-dimensional time series data (e.g. multiple attributes such as wind availability, solar availability, and electricity demand) in a single aggregation process.
 The software is applicable to general time series datasets and lends itself well to a multitude of application areas within the field of time series data mining.
 ``TimeSeriesClustering`` was originally developed to perform time series aggregation for energy systems optimization problems. Because of the software's origin, many of the examples in this work stem from the field of energy systems optimization.
 
 ## General package features
 
 The unique design of ``TimeSeriesClustering`` allows for scientific comparison of the performance of different time-series aggregation methods, both in terms of the statistical error measure and in terms of its impact on the application outcome.
-The clustering methods that are implemented in ``TimeSeriesClustering`` follow the framework presented by @Teichgraeber:2019, and the extreme value selection methods follow the framework presented by @Lindenmeyer:2019. Using these frameworks allows ``TimeSeriesClustering`` to be generally extensible to new aggregation methods in the future.
+The clustering methods that are implemented in ``TimeSeriesClustering`` follow the framework presented by @Teichgraeber:2019, and the extreme value selection methods follow the framework presented by @Lindenmeyer:2020. Using these frameworks allows ``TimeSeriesClustering`` to be generally extensible to new aggregation methods in the future.
 
 The following are the key features that ``TimeSeriesClustering`` provides. Implementation details can be found in the software's documentation.
 
@@ -111,7 +111,7 @@ A related class of problems that ``TimeSeriesClustering`` can be useful to is sc
 Furthermore, ``TimeSeriesClustering`` could be used in operational contexts such as developing operational strategies for typical days, or aggregating repetitive operating conditions for use in model predictive control.
 Because it keeps track of the chronology of the periods, it can also be used to calculate transition probabilities between clustered periods for Markov chain modeling.
 
-``TimeSeriesClustering`` has been used in several research projects to date. It has been used to compare both conventionally-used clustering methods and shape-based clustering methods and their characteristics [@Teichgraeber:2019], and also to compare extreme value selection methods [@Lindenmeyer:2019].
+``TimeSeriesClustering`` has been used in several research projects to date. It has been used to compare both conventionally-used clustering methods and shape-based clustering methods and their characteristics [@Teichgraeber:2019], and also to compare extreme value selection methods [@Lindenmeyer:2020].
 It has also been used to analyze temporal modeling detail in energy systems modeling with high renewable energy penetration [@Kuepper:2019].
 ``TimeSeriesClustering`` also serves as input to [``CapacityExpansion``](https://github.com/YoungFaithful/CapacityExpansion.jl), a scalable capacity expansion model in Julia.
 Furthermore, ``TimeSeriesClustering`` has been used as an educational tool. It is frequently used for class projects in the Stanford University course "Optimization of Energy Systems", and has also served as a basis for the capacity expansion studies evaluated in homeworks for the Stanford University course "Advanced Methods in Modeling for Climate and Energy Policy".