spelling corrected

R/rucrdtw-package.r

@@ -1,6 +1,6 @@
 #' rucrdtw: Fast time series subsequence search in R
 #'
-#' @description Dynamic Time Warping (DTW) methods provide algorithms to optimally map a given time series onto all or part of another time series. The remaining cumulative distance between the series after the alignement is a useful distance metric in time series data mining applications for tasks such as classification, clustering, and anomaly detection. A broad suite of DTW algorithms is implemented in R in the \strong{dtw} package (Giorgino 2009).
+#' @description Dynamic Time Warping (DTW) methods provide algorithms to optimally map a given time series onto all or part of another time series. The remaining cumulative distance between the series after the alignment is a useful distance metric in time series data mining applications for tasks such as classification, clustering, and anomaly detection. A broad suite of DTW algorithms is implemented in R in the \strong{dtw} package (Giorgino 2009).
 #'
 #' Calculating a DTW alignment is computationally relatively expensive, and as a consequence DTW is often a bottleneck in time series data mining applications. The UCR Suite (Rakthanmanon et al. 2012) provides a highly optimized algorithm for best-match subsequence searches that avoids unnecessary distance computations and thereby enables fast DTW and Euclidean Distance queries even in data sets containing trillions of observations.
 #'

paper.md

@@ -14,7 +14,7 @@ bibliography: vignettes/rucrdtw.bib
 ---
 
 # Summary
-Dynamic Time Warping (DTW) methods provide algorithms to optimally map a given time series onto all or part of another time series [@berndt1994using]. The remaining cumulative distance between the series after the alignement is a useful distance metric in time series data mining applications for tasks such as classification, clustering, and anomaly detection. 
+Dynamic Time Warping (DTW) methods provide algorithms to optimally map a given time series onto all or part of another time series [@berndt1994using]. The remaining cumulative distance between the series after the alignment is a useful distance metric in time series data mining applications for tasks such as classification, clustering, and anomaly detection. 
 
 Calculating a DTW alignment is computationally relatively expensive, and as a consequence DTW is often a bottleneck in time series data mining applications. The [UCR Suite](http://www.cs.ucr.edu/~eamonn/UCRsuite.html) [@rakthanmanon2012searching] provides a highly optimized algorithm for best-match subsequence searches that avoids unnecessary distance computations and thereby enables fast DTW and Euclidean Distance queries even in data sets containing trillions of observations.
 

vignettes/using_rucrdtw.Rmd

@@ -11,7 +11,7 @@ bibliography: rucrdtw.bib
 ---
 
 ## Introduction
-Dynamic Time Warping (DTW) methods provide algorithms to optimally map a given time series onto all or part of another time series [@berndt1994using]. The remaining cumulative distance between the series after the alignement is a useful distance metric in time series data mining applications for tasks such as classification, clustering, and anomaly detection. 
+Dynamic Time Warping (DTW) methods provide algorithms to optimally map a given time series onto all or part of another time series [@berndt1994using]. The remaining cumulative distance between the series after the alignment is a useful distance metric in time series data mining applications for tasks such as classification, clustering, and anomaly detection. 
 
 Calculating a DTW alignment is computationally relatively expensive, and as a consequence DTW is often a bottleneck in time series data mining applications. The [UCR Suite](http://www.cs.ucr.edu/~eamonn/UCRsuite.html) [@rakthanmanon2012searching] provides a highly optimized algorithm for best-match subsequence searches that avoids unnecessary distance computations and thereby enables fast DTW and Euclidean Distance queries even in data sets containing trillions of observations.
 
@@ -89,7 +89,7 @@ legend("topright", legend = c("query", "DTW match", "ED match"), col=c("red", "b
 ```
 
 ## Comparison with a naive DTW sub-sequence search
-We can compare the speed-up achived with the UCR algorithm by comparing it to a naive sliding-window comparison with the `dtw` function from the [`dtw` package](https://CRAN.R-project.org/package=dtw) [@giorgino2009computing]. We create another time series and load `dtw`.
+We can compare the speed-up achieved with the UCR algorithm by comparing it to a naive sliding-window comparison with the `dtw` function from the [`dtw` package](https://CRAN.R-project.org/package=dtw) [@giorgino2009computing]. We create another time series and load `dtw`.
 
 ```{r dtw-comparison, message=FALSE}
 set.seed(123)