Merge pull request #101 from holgerteichgraeber/paper-comments-Youngfaithful

Comments on paper

Author: holgerteichgraeber

paper/paper.md

@@ -40,11 +40,11 @@ To the best of our knowledge, time-series aggregation has been included in two o
 @TSAM provides an implementation of several time-series aggregation methods in python.
 Calliope [@Pfenninger:2018] is a capacity expansion modeling software in python that includes time-series aggregation for the use case of generation and transmission capacity expansion modeling.
 
-``ClustForOpt`` is the first package to provide time-series aggregation in Julia. This is advantageous because it can be used in conjunction with the JuMP [@Dunning:2017] package in Julia, which provides an excellent modeling language for optimization problems. Furthermore, ClustForOpt includes both clustering and extreme value selection and integrates them into the same output type. This is important in order to retain the characteristics of the time-series that are relevant to many optimization problems.
+``ClustForOpt`` is the first package to provide time-series aggregation in Julia. This is advantageous because it can be used in conjunction with the JuMP [@Dunning:2017] package in Julia, which provides an excellent modeling language for optimization problems. Furthermore, ``ClustForOpt`` includes both clustering and extreme value selection and integrates them into the same output type. This is important in order to retain the characteristics of the time-series that are relevant to many optimization problems.
 
 At this point, we would like to point to the key features that ``ClustForOpt`` provides. Implementation details can be found in the software's documentation.
 
-- *The type system*: The data type (called struct in julia) ``ClustData`` stores all time-series data in a common format. Besides the data itself, it automatically processes and stores information such as number of periods, number of time steps per period, and the chronology of the periods, which are relevant to formulating the sets of the optimization problem later. The data type ``ClustResult`` additionally stores information relevant for evaluating clustering performance. These data types make ``ClustForOpt`` to be easily integrated with any optimization problem and analysis.
+- *The type system*: The data type (called struct in julia) ``ClustData`` stores all time-series data in a common format. Besides the data itself, it automatically processes and stores information such as number of periods, the number of time steps per period, which is called the period length, and the chronology of the periods, which are relevant to formulating the sets of the optimization problem later. The data type ``ClustResult`` additionally stores information relevant for evaluating clustering performance. These data types make ``ClustForOpt`` to be easily integrated with any optimization problem and analysis.
 
 - *The aggregation methods*: The most commonly used clustering methods and extreme value selection methods are implemented with a common interface, allowing for simple comparison of these methods on a given data set and optimization problem.