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D. Poskitt (2019)
On Singular Spectrum Analysis And Stepwise Time Series ReconstructionJournal of Time Series Analysis, 41
N. Golyandina, A. Korobeynikov, A. Zhigljavsky (2018)
Singular Spectrum Analysis with R
X. Nguyen, J. Epps, J. Bailey (2010)
Information Theoretic Measures for Clusterings Comparison: Variants, Properties, Normalization and Correction for ChanceJ. Mach. Learn. Res., 11
deaths <-1 -abs(W.corr(ssa.object = s.deaths, groups = 1:36))
N. Golyandina, A. Korobeynikov (2012)
Basic Singular Spectrum Analysis and forecasting with RComput. Stat. Data Anal., 71
(1999)
Classification, 2nd ed.
Malika Charrad, Nadia Ghazzali, V. Boiteau, A. Niknafs (2014)
NbClust: An R Package for Determining the Relevant Number of Clusters in a Data SetJournal of Statistical Software, 61
(2021)
Flexible Procedures for Clustering
A. Korobeynikov (2009)
Computation- and Space-Efficient Implementation of SSAArXiv, abs/0911.4498
M. Halkidi (2020)
Hierarchical ClusteringClustering
S. Lahmiri (2018)
Minute-ahead stock price forecasting based on singular spectrum analysis and support vector regressionAppl. Math. Comput., 320
A. Zhigljavsky (2013)
Singular Spectrum Analysis for Time Series
P. Brockwell, R. Davis (1998)
Introduction to time series and forecasting
A. Saayman, J. Klerk (2019)
Forecasting tourist arrivals using multivariate singular spectrum analysisTourism Economics, 25
S. Sanei, Hossein Hassani (2015)
Singular Spectrum Analysis of Biomedical Signals
(2021)
Time Series Data Library. Available online: http://data.is/TSDLdemo (accessed on 10 May 2021)
M. Cugmas, A. Ferligoj (2015)
On comparing partitionsInternational Federation of Classification Societies
Hossein Hassani, M. Yeganegi, E. Silva (2018)
A New Signal Processing Approach for Discrimination of EEG RecordingsStats
L. Kaufman, P. Rousseeuw (1991)
Finding Groups in Data: An Introduction to Cluster Analysis
-#Distance matrix for FORT and Deaths series
-#Extracting the Deaths series from 'datasets' package data
Hossein Hassani, A. Rúa, E. Silva, D. Thomakos (2019)
Monthly forecasting of GDP with mixed-frequency multivariate singular spectrum analysisInternational Journal of Forecasting
-#Clustering by a specified linkage, for example
-#Time series plots (Figure 8) layout(matrix(1:2, ncol = 2)) par(mar = c(2, 2, 2, 2)) plot.ts(fort, ylab = '', xlab = '', main = 'FORT series') par(mar = c(2, 2, 2, 2)) plot.ts(USAccDeaths
M. Bilancia, F. Campobasso (2010)
Airborne Particulate Matter and Adverse Health Events: Robust Estimation of Timescale Effects
Alexander Gates, Yong-Yeol Ahn (2017)
The Impact of Random Models on Clustering SimilaritybioRxiv
(2018)
Singular Spectrum Analysis Using R
Flexible Procedures for Clustering. R Package Version 2.2-9
N. Golyandina, A. Korobeynikov, Alexander Shlemov, K. Usevich (2013)
Multivariate and 2D Extensions of Singular Spectrum Analysis with the Rssa Package
-#Plot of w-correlation matrix (Figures 9 and 10) plot(s.fort, type = "wcor", groups = 1:30, grid = 14, lty = 2) plot(s.deaths, type = "wcor
(2021)
A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2021
Seyed Safi, M. Pooyan, A. Nasrabadi (2018)
Improving the performance of the SSVEP-based BCI system using optimized singular spectrum analysis (OSSA)Biomed. Signal Process. Control., 46
-#Decomposition Stage s.fort <-ssa(fort, L = 84, neig = 84) s.deaths <-ssa
(2016)
Hierarchical Clustering. In Handbook of Cluster
(2021)
Cluster: Cluster Analysis Basics and Extensions
Xiaorong Ding, Bryan Yan, Walter Karlen, Yuan-Ting Zhang, H. Tsang (2019)
Pulse transit time based respiratory rate estimation with singular spectrum analysisMedical & Biological Engineering & Computing, 58
(2013)
Applied Multivariate Statistical Analysis, 6th ed.; Pearson Education Ltd
(2020)
Singular Spectrum Analysis for Time Series, 2nd ed.; Springer Briefs in Statistics
M. Khan, D. Poskitt (2017)
Forecasting stochastic processes using singular spectrum analysis: Aspects of the theory and applicationInternational Journal of Forecasting, 33
Time Series Data Library
R. Team (2014)
R: A language and environment for statistical computing.MSOR connections, 1
fpc : Flexible Procedures for Clustering . R Package Version 2 . 2 - 9 . 2020
A. Rukhin (2002)
Analysis of Time Series Structure SSA and Related TechniquesTechnometrics, 44
R. Mahmoudvand, P. Rodrigues (2020)
Predicting the Brexit outcome using singular spectrum analysis, 1
W. Härdle, L. Simar (2003)
Applied Multivariate Statistical Analysis
J. Arteche, Javier García‐Enríquez (2017)
Singular Spectrum Analysis for signal extraction in Stochastic Volatility modelsEconometrics and Statistics, 1
<-function(ssa.object, groups){
N. Golyandina (2019)
Particularities and commonalities of singular spectrum analysis as a method of time series analysis and signal processingWiley Interdisciplinary Reviews: Computational Statistics, 12
Article Comparative Assessment of Hierarchical Clustering Methods for Grouping in Singular Spectrum Analysis 1, 2 3 Hossein Hassani * , Mahdi Kalantari and Christina Beneki Research Institute of Energy Management and Planning (RIEMP), University of Tehran, Tehran 1417466191, Iran Department of Statistics, Payame Noor University, Tehran 19395-4697, Iran; kalantarimahdi@gmail.com Department of Tourism, Faculty of Economic Sciences, Ionian University, 49100 Corfu, Greece; benekic@ionio.gr * Correspondence: hassani.stat@gmail.com Abstract: Singular spectrum analysis (SSA) is a popular ﬁltering and forecasting method that is used in a wide range of ﬁelds such as time series analysis and signal processing. A commonly used approach to identify the meaningful components of a time series in the grouping step of SSA is the utilization of the visual information of eigentriples. Another supplementary approach is that of employing an algorithm that performs clustering based on the dissimilarity matrix deﬁned by weighted correlation between the components of a time series. The SSA literature search revealed that no investigation has compared the various clustering methods. The aim of this paper was to compare the effectiveness of different hierarchical clustering linkages to identify the appropriate groups in the grouping step of SSA. The comparison was performed based on the corrected Rand (CR) index as a comparison criterion that utilizes various simulated series. It was also demonstrated via two real-world time series how one can proceed, step-by-step, to conduct grouping in SSA using a hierarchical clustering method. This paper is supplemented with accompanying R codes. Keywords: time series; basic SSA; hierarchical clustering; corrected Rand index Citation: Hassani, H.; Kalantari, M.; Beneki, C. Comparative Assessment of Hierarchical Clustering Methods for Grouping in Singular Spectrum Analysis. AppliedMath 2021, 1, 18–36. 1. Introduction https://doi.org/10.3390/ Singular spectrum analysis (SSA) is a model-free technique that decomposes a time appliedmath1010003 series into a number of meaningful components. Owing to its widespread applications in various ﬁelds, this non-parametric method has received much attention in recent years. Received: 23 October 2021 As evidence, examples of the wide variety of SSA applications can be found in [1–9]. A Accepted: 29 November 2021 whole and precise detailed summary of the theory and applications of SSA can be found in Published: 14 December 2021 [10,11]. Additionally, there are other books devoted to SSA; for example, Refs. [12–14]. A comprehensive review of SSA and the description of its modiﬁcations and extensions can Publisher’s Note: MDPI stays neutral be found in [15]. with regard to jurisdictional claims in One of the challenging problems of SSA is identifying the interpretable components published maps and institutional afﬁl- of a time series. The conventional method of detecting interpretable components such as iations. trend and periodic components, is to apply the information contained in singular values and singular vectors of the trajectory matrix of a time series. In this approach, a screen plot of the singular values, one-dimensional and two-dimensional ﬁgures of the singular vectors, and the matrix of the absolute values of the weighted correlations can provide a Copyright: © 2021 by the authors. visual method to identify the appropriate components. More details on group identiﬁcation Licensee MDPI, Basel, Switzerland. based on visual tools can be found in [11,14]. This article is an open access article There is another supplementary approach to identifying the meaningful components distributed under the terms and of a time series that performs clustering based on a dissimilarity matrix deﬁned by weighted conditions of the Creative Commons correlation between elementary components. In this simple method, ﬁrst, the similarity Attribution (CC BY) license (https:// of elementary components is measured by means of the weighted correlations between creativecommons.org/licenses/by/ them. Then, a proximity matrix is constructed using weighted correlations. Finally, the 4.0/). AppliedMath 2021, 1, 18–36. https://doi.org/10.3390/appliedmath1010003 https://www.mdpi.com/journal/appliedmath AppliedMath 2021, 1 19 elementary components are grouped via distance-based clustering techniques such as hierarchical methods. Although this approach is interesting, it has not yet been established which hierarchical clustering method can provide an accurate and reasonable grouping. For instance, the hierarchical clustering with complete linkage was used in [16], while the reason for selecting the complete linkage was not clear. Since, to the best of our knowledge, no one has conducted a comparison study to determine an adequate hierarchical clustering method on the basis of weighted correlations, the present research has tried to ﬁll this gap. In this investigation, we sought to compare the performance of several hierarchical clustering linkages in order to ﬁnd a proper linkage. This paper is divided into ﬁve sections. In Section 2, the theoretical background and general scheme of basic SSA are reviewed. Furthermore, a brief overview of hierarchical clustering methods and measure of similarity between clusters that is exploited in this research is outlined in this section. Section 3 is dedicated to comparing the performance of hierarchical clustering linkages via simulating a variety of synthetic time series. Two case studies are proposed in Section 4 using real-world time series datasets. In this section, it is also demonstrated how one can proceed, step-by-step, to conduct grouping in SSA using a hierarchical clustering method. Some conclusions and the discussion are given in Section 5 and supplementary R codes are presented in the Appendix A. 2. Theoretical Background In this section, we ﬁrst brieﬂy explain the theory underlying Basic SSA which is the most fundamental version of the SSA technique. Then, the hierarchical clustering methods that are of interest to this study were reviewed. 2.1. Review of Basic SSA The basic SSA consists of four steps which are similar to those of other versions of SSA. These steps are brieﬂy described below and in doing so, we mainly follow [11,14]. More detailed information on the theory of the SSA method can be found in [10]. Step 1: Embedding. In this step, the time series X = fx , . . . , x g is transformed into the 1 N L K matrix X, whose columns comprise X , . . . , X , where X = (x , . . . , x ) 1 K i i i+L1 2 R and K = N L + 1. The matrix X is called the trajectory matrix. This matrix is a Hankel matrix in the sense that all the elements on the anti-diagonals i + j = const. are equal. The embedding step only has one parameter L, which is called the window length or embedding dimension. The window length is commonly chosen such that 2 L N/2 where N is the length of the time series X. Step 2: Decomposition. In this step, the trajectory matrix X is decomposed into a sum of rank-one matrices using the conventional singular value decomposition (SVD) procedure. The eigenvalues of XX were denoted by l , . . . , l in decreasing 1 L order of magnitude (l l 0) and by U , . . . , U , the eigenvectors of 1 L 1 L the matrix XX corresponding to these eigenvalues. If d = maxfi, such that l > 0g = rank(X), then the SVD of the trajectory matrix can be written as X = X + + X , (1) 1 d p p T T where X = l U V and V = X U / l (i = 1, . . . , d). The collection i i i i i i i ( l , U , V ) is called the ith eigentriple of the SVD. i i i Step 3: Grouping. The aim of this step is to group the components of (1). Let I = fi , . . . , i g be the subset of indices f1, . . . , dg. Then, the resultant matrix X 1 I corresponding to the group I is deﬁned as X = X + + X , that is, summing I i i the matrices within each group. With the SVD of X, the split of the set of indices f1, . . . , dg into the m disjoint subsets I , . . . , I corresponds to the following 1 m decomposition: X = X + + X . (2) I I If I = fig, for i = 1, . . . , d, then the corresponding grouping is called elementary. i AppliedMath 2021, 1 20 Step 4: Diagonal Averaging. The main goal of this step is to transform each matrix X of the grouped matrix decomposition (2) into a Hankel matrix, which can subsequently be converted into a new time series of length N. Let A be an L K matrix with elements a , 1 i L, 1 j K. By diagonal averaging, the i j matrix A is transferred into the Hankel matrix HA with the elements e a over the anti-diagonals (1 s N) using the following formula: lk e a = , (3) j A j (l,k)2 A where A = f(l, k) : l + k = s + 1, 1 l L, 1 k Kg and j A j denotes the s s number of elements in the set A . By applying diagonal averaging (3) to all the matrix components of (2), the following expansion is obtained: X = X + + e e X , where X = HX , j = 1, . . . , m. This is equivalent to the decomposition I I I j j (k) of the initial series X = fx , . . . , x g into a sum of m series: x = å xe (t = N t k=1 t (k) (k) e e 1, . . . , N), where X = fxe , . . . , xe g corresponds to the matrix X . k I 1 N k Usually, Steps 1 and 2 of the SSA technique are called the Decomposition Stage, and Steps 3 and 4 are called the Reconstruction Stage. There are many software applications to implement SSA such as Caterpillar-SSA and SAS/ETS. In this investigation, we apply the free available R package Rssa to conduct the decomposition and reconstruction stages of SSA. More details on this package can be found in [17–19]. There is a fundamental concept in SSA called separability that indicates the quality of the decomposition by determining how well different components of a time series are separated from each other. Let us describe this important concept in more detail. Let L,K L,K A = a and B = b be L K matrices. The inner product of two matrices A i j i j i,j=1 i,j=1 and B is deﬁned as L K hA, Bi = a b . å å i j i j i=1 j=1 Based on this deﬁnition of inner product, the Frobenius norm of matrices A and B p p e e is kAk = hA, Ai, kBk = hB, Bi. Now, suppose that X = HX and X = HX i i j j F F e e are, respectively, the trajectory matrices of two reconstructed series X and X , which are i j obtained from the diagonal averaging step of SSA based on the elementary grouping I = fig. The measure of approximate separability between two reconstructed series X i i and X is the weighted correlation or w-correlation, which is deﬁned as D E e e X , X i j (w) r = . i j e e X X i j F F (w) Geometrically, r is equal to the cosine of the angle between the two trajectory matrices i j e e e e of reconstructed components X and X . Using the Hankel property of matrices X and X , i j i j it can be easily shown that the w-correlation is equivalent to the following form [10]: (i) (j) å w x x (w) k k=1 k k r = q q , (4) i j (i) (j) N N 2 2 å w (x ) å w (x ) k k k=1 k k=1 k where w = minfk, L, N k + 1g. It is noteworthy that the weight w is equal to the number of times the element x appears in the trajectory matrix X of the initial time series (w) e e X = fx , . . . , x g. If r is large, then it can be deduced that X and X are highly 1 N i j i j correlated. Hence, these two components should be included in the same group. However, a small value of the w-correlation can indicate the good separability of the components. AppliedMath 2021, 1 21 (w) Consequently, an absolute value of the w-correlation close to zero ( r ' 0) would imply i j e e a high separation between two reconstructed components X and X . i j 2.2. Hierarchical Clustering Methods In this paper, we apply hierarchical clustering methods to cluster the eigentriples in the grouping step of SSA. Hierarchical clustering is a widely used clustering method that connects objects to form clusters based on their distance. This distance-based method requires the deﬁnition of a dissimilarity measure (or distance) between objects. There is a multitude of dissimilarity measures available in the literature that can be used in hierarchi- cal clustering, such as Euclidean, Manhattan, Canberra, Minkowski, binary and maximum distances. Owing to the fact that different distances result in different clusters, some im- portant aspects should be considered in the choice of the similarity measure. The main considerations include the nature of the variables (discrete, continuous, binary), the scales of measurement (nominal, ordinal, interval, ratio), and subject matter knowledge [20]. In this article, we deﬁne the dissimilarity measure between the two reconstructed components (w) e e X and X as d = 1 r . As illustrated in [14], the Algorithm 1 of auto-grouping based i j i j i j on clustering methods can be written as follows: Algorithm 1 Auto-grouping using clustering methods. Require: Time series X = fx , . . . , x g, window length (L), number of groups; 1 N 1: Decompose time series into elementary components. (w) e e 2: Compute w-correlation (r ) between elementary reconstructed series X and X i j i j using (4). e e 3: Compute dissimilarity between elementary reconstructed series X and X as d = i j i j (w) 1 r . i j 4: Construct the distance matrix D = [d ]. i j 5: Use a method of clustering to the distance matrix D. 6: Obtain the given number of groups from the results of cluster analysis. It should be noted that the automatic identiﬁcation of SSA components has a limitation. As mentioned in [11], this limitation is assuming that the components to be identiﬁed are (approximately) separated from the rest of the time series. There are generally two types of hierarchical clustering approaches: • Divisive. In this approach, an initial single cluster of objects is divided into two clusters such that the objects in one cluster are far from the objects in the other cluster. The process proceeds by splitting the clusters into smaller and smaller clusters until each object forms a separate cluster [20,21]. We implemented this method in our research via the function diana from the cluster package [22] of the freely available statistical R software [23]. • Agglomerative. In this approach, the individual objects are initially treated as a clus- ter, and then the most similar clusters are merged according to their similarities. This procedure proceeds by successive fusions until a single cluster is eventually obtained containing all the objects [20,21]. Several agglomerative hierarchical clustering meth- ods are employed in this paper including single, complete, average, mcquitty, median, centroid and Ward. There are two algorithms ward.D and ward.D2 for the Ward method, which are available in R packages such as stats and NbClust [24]. By implementing the ward.D2 algorithm, the dissimilarities are squared before the cluster updates. All of the agglomerative hierarchical clustering methods that were implemented in this research can be attained via the function hclust from the stats package of R software. More information about hierarchical clustering algorithms can be found in [20,21,25,26]. AppliedMath 2021, 1 22 2.3. Cluster Validation Measure In this paper, we used the corrected Rand (CR) index to measure the similarity between the grouping output obtained with a hierarchical clustering method and a given “correct” grouping. Let us explain the CR index in more detail. Given an n element set S, suppose X = fX , X , . . . , X g and Y = fY , Y , . . . , Y g represent two different partitions of S. The 1 2 r 1 2 c information on class overlap between the two partitions X and Y can be written in the form of Table 1: Table 1. Notations for the CR index. Y Y . . . Y sums 1 2 c X n n . . . n n 1 11 12 1c 1 X n n . . . n n 2 21 22 2c 2 . . . . . . . . . . . . . . . . X n n . . . n n r r1 r2 rc r sums n n . . . n n = n 1 2 c Where n denotes the number of objects that are common to classes X and Y , n and i j i j i n refer, respectively, to the number of objects in classes X (sum of row i) and Y (sum of j i j column j). Based on the notations provided in Table 1, the CR index is deﬁned as follows: n n n n i j j å ( ) å ( )å ( )/( ) i j i j 2 2 2 2 h i CR = n n 1 n n n i j i j ( ) + ( ) ( ) ( )/( ) å å å å i j i j 2 2 2 2 2 2 The CR index is an external cluster validation index and can be implemented in the R function cluster.stats from the fpc package [27]. This index varies from 1 (no similarity) to 1 (perfect similarity) and if it displays high values then this indicates great similarity between the two clusters. However, this index does not take into consideration the numbers and contributions of time series components with the wrong partition. More details on this index can be found in [25,28–30]. 3. Simulation Study In this section, the performances of agglomerative and divisive hierarchical clustering methods were evaluated by applying them to various simulated time series with different patterns. In the following simulated series, the normally distributed noise with zero mean (# ) is added to each point of the generated series: (a) Exponential: y = exp(0.03t) + # , t = 1, 2, . . . , 100 t t (b) Sine: y = sin(2pt/6) + # , t = 1, 2, . . . , 100 t t (c) Linear + Sine: y = 50 0.2t + 8 sin(2pt/4) + # , t = 1, 2, . . . , 100 t t (d) Sine + Sine: y = 2 sin(2pt/3) + sin(2pt/12) + # , t = 1, 2, . . . , 200 t t (e) Exponential Cosine: y = exp(0.02t) cos(2pt/12) + # , t = 1, 2, . . . , 200 t t (f) Exponential + Cosine: y = exp(0.025t) + 30 cos(2pt/12) + # , t = 1, 2, . . . , 200 t t Various signal-to-noise ratios (SNRs) including 0.25, 0.75, 5, and 10 were employed to assess the impact of noise levels on grouping results. The simulation was repeated 5000 times for each SNR of each scenario (a)–(f) and then, the average of the CR index was computed. In order to enable better comparison, the dashed horizontal line y = 1 was added to all ﬁgures of the CR index. To observe the structure of each simulated series, the simulation was performed once with SN R = 10. The time series plots of the simulated series are depicted in Figure 1. As can be seen in this ﬁgure, the simulated series (a)–(f) have various patterns such as an upward and downward trends, and periodicity. AppliedMath 2021, 1 23 (a) Exponential (b) Sine (c) Linear+Sine 0 20 40 60 80 100 0 20 40 60 80 100 0 20 40 60 80 100 (d) Sine+Sine (e) Exponential*Cosine (f) Exponential+Cosine 0 50 100 150 200 0 50 100 150 200 0 50 100 150 200 Figure 1. Time series plot of the simulated series. In Table 2, the “correct” groups for each of the simulated series are determined based on the rank of the trajectory matrix of the simulated series and the grouping rules proposed in [10]. A major step in hierarchical clustering is the decision on where to cut the dendrogram. In this simulation study, we used the number of groups (or clusters), which is reported in Table 2, as a cutting rule of the dendrogram. Table 2. Correct groups of the simulated series. Simulated Series Correct Groups The Number of Clusters Exponential f1g,f2, . . . , Lg 2 Sine f1, 2g,f3, . . . , Lg 2 Linear+sine f1, 2g,f3, 4g,f5, . . . , Lg 3 Sine+Sine f1, 2g,f3, 4g,f5, . . . , Lg 3 ExponentialCosine f1, 2g,f3, . . . , Lg 2 Exponential+Cosine f1g,f2, 3g,f4, . . . , Lg 3 Figure 2 shows the CR index for the exponential series (case a) which are computed for various hierarchical clustering algorithms and different values of window length (L). It can be concluded from this ﬁgure that the ward.D and ward.D2 methods have the worst performance at each level of SNR. In addition, the similarity between the grouping by the complete method and “correct” groups decrease as L increases. However, the other methods exhibit good performance in detecting the “correct” groups, especially for a larger SNR. In the case of SN R = 0.25, the methods of centroid, single, and average are better than the methods of mcquitty, median, and diana. Figure 3 depicts the CR index for the Sine series (case b). It can be deduced from this ﬁgure that the ward.D and ward.D2 methods are unable to identify the “correct” groups at each level of the SNR when L 18. In addition, it seems that the capability of the complete method to increase as the SNR reaches high levels, although its capability for moderate values of L is better than other values when SN R > 1. Moreover, the diana method generally outperforms the ward.D, ward.D2, and complete methods. When SN R < 1, the methods of centroid, single, and average provide more reliable outputs compared to the −3 −2 −1 0 1 2 3 0 5 10 15 20 −40 −20 0 20 40 −1.0 −0.5 0.0 0.5 1.0 −50 0 50 100 150 20 30 40 50 60 AppliedMath 2021, 1 24 mcquitty, median, and diana methods. Furthermore, it seems that there is not a signiﬁcant difference among the centroid, single, average, and mcquitty techniques when SN R > 1. SNR=0.25 SNR=0.75 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 1.00 ● ● ● ● ● 1.00 0.75 0.75 Methods 0.50 0.50 ● average centroid 0.25 0.25 complete 0.00 0.00 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● diana 5 10 15 20 25 30 35 40 45 50 5 10 15 20 25 30 35 40 45 50 SNR=5 SNR=10 mcquitty 1.00 ● ● ● ● ● ● ● ● ● ● 1.00 ● ● ● ● ● ● ● ● ● ● median 0.75 0.75 single ward.D 0.50 0.50 ward.D2 0.25 0.25 0.00 0.00 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 5 10 15 20 25 30 35 40 45 50 5 10 15 20 25 30 35 40 45 50 Window Length (L) Window Length (L) Figure 2. CR index for the Exponential series (case a). SNR=0.25 SNR=0.75 1.00 1.00 ● ● ● ● ● ● ● ● ● ● ● 0.75 0.75 Methods 0.50 0.50 average ● centroid 0.25 0.25 ● complete 0.00 ● 0.00 ● ● ● ● ● ● ● ● diana 6 12 18 24 30 36 42 48 6 12 18 24 30 36 42 48 SNR=5 SNR=10 mcquitty ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● 1.00 1.00 median 0.75 0.75 single ● ward.D 0.50 0.50 ward.D2 0.25 0.25 ● ● 0.00 ● 0.00 ● ● ● ● ● ● ● ● ● 6 12 18 24 30 36 42 48 6 12 18 24 30 36 42 48 Window Length (L) Window Length (L) Figure 3. CR index for the Sine series (case b). In Figure 4, the CR index for the Linear+Sine series (case c) is presented. Once again, it can be concluded from this ﬁgure that the ward.D and ward.D2 methods have the worst performance at each level of SNR. The other results that can be concluded from this ﬁgure are similar to those of the Sine series (case b) and are hence not repeated here. CR CR CR CR AppliedMath 2021, 1 25 As we can see in Figure 4, the CR index of any of the methods could not obtain the value of one. This is true for all cases of SNR. In other words, none of the methods could exactly identify the correct groups at each level of SNR. This is due to the fact that the rank of linear time series is equal to 2 and therefore, it generates two non-zero eigenvalues. However, the second eigenvalue of the linear time series is relatively small and consequently, it is probably mixed with noise and can therefore not be identiﬁed, especially for a high level of noise. SNR=0.25 SNR=0.75 1.00 1.00 0.75 0.75 Methods 0.50 0.50 average centroid 0.25 0.25 complete 0.00 0.00 diana 8 12 16 20 24 28 32 36 40 44 48 8 12 16 20 24 28 32 36 40 44 48 mcquitty SNR=5 SNR=10 1.00 1.00 median 0.75 0.75 single ward.D 0.50 0.50 ward.D2 0.25 0.25 0.00 0.00 8 12 16 20 24 28 32 36 40 44 48 8 12 16 20 24 28 32 36 40 44 48 Window Length (L) Window Length (L) Figure 4. CR index for the Linear+Sine series (case c). In Figure 5, the CR index for the Sine+Sine series (case d) is shown. Once again, the ward.D and ward.D2 methods present poor results at each level of the SNR. When SN R < 1, the single, centroid and average methods provide better results in comparison to the other methods. Furthermore, it seems that these methods can exactly identify the correct groups if SN R > 1. Figure 6 depicts the CR index for the ExponentialCosine series (case e). As can be seen in this ﬁgure, the ward.D and ward.D2 methods cannot detect the “correct” groups at each level of the SNR. The single and average methods show better outputs when SN R < 1. Additionally, in the case of SN R > 1, it seems that there is not a signiﬁcant difference among the single, average, and mcquitty approaches. In Figure 7, the CR index for the Exponential+Cosine series (case f) is presented. Similar to the previous results, the ward.D and ward.D2 methods cannot provide effective results at each level of the SNR. However, it seems that the centroid, single, and average methods are better than the other methods—especially for SN R < 1. The outputs of the simulation study clearly indicate that the ward.D and ward.D2 methods cannot detect the “correct” groups at each level of the SNR. In addition, these outputs reveal that choosing the best hierarchical clustering linkage is not straightforward— it depends on the structure of a time series and the level of the contribution of noise. In order to have a general overview of the simulation results, the ﬁndings of the simulation study are summarized in Table 3. Based on these ﬁndings, it seems that the single, centroid, average and mcquitty methods can identify the “correct” groups better than the other linkages. CR CR AppliedMath 2021, 1 26 SNR=0.25 SNR=0.75 1.00 1.00 0.75 0.75 Methods 0.50 0.50 average centroid 0.25 0.25 complete 0.00 0.00 diana 12 24 36 48 60 72 84 96 12 24 36 48 60 72 84 96 mcquitty SNR=5 SNR=10 1.00 1.00 median 0.75 0.75 single ward.D 0.50 0.50 ward.D2 0.25 0.25 0.00 0.00 12 24 36 48 60 72 84 96 12 24 36 48 60 72 84 96 Window Length (L) Window Length (L) Figure 5. CR index for the Sine+Sine series (case d). SNR=0.25 SNR=0.75 1.00 1.00 0.75 0.75 ● Methods 0.50 0.50 average centroid 0.25 0.25 complete 0.00 0.00 ● ● ● ● ● ● ● ● ● ● ● ● ● ● diana 12 24 36 48 60 72 84 96 12 24 36 48 60 72 84 96 SNR=5 SNR=10 mcquitty ● ● ● ● 1.00 1.00 ● ● ● ● median 0.75 0.75 ● single ● ● ward.D 0.50 0.50 ward.D2 0.25 0.25 0.00 ● ● ● 0.00 ● ● ● ● ● ● ● ● ● ● ● 12 24 36 48 60 72 84 96 12 24 36 48 60 72 84 96 Window Length (L) Window Length (L) Figure 6. CR index for the ExponentialCosine series (case e). CR CR CR CR AppliedMath 2021, 1 27 SNR=0.25 SNR=0.75 1.00 1.00 ● ● ● ● 0.75 0.75 ● ● ● ● Methods 0.50 0.50 average centroid 0.25 0.25 complete 0.00 ● ● 0.00 ● ● ● ● ● ● ● ● ● ● diana 12 24 36 48 60 72 84 96 12 24 36 48 60 72 84 96 mcquitty SNR=5 SNR=10 1.00 ● ● ● ● ● ● ● ● 1.00 ● ● ● ● ● ● ● ● median 0.75 0.75 single ward.D 0.50 0.50 ward.D2 0.25 0.25 0.00 0.00 ● ● ● ● ● ● ● ● ● ● ● ● 12 24 36 48 60 72 84 96 12 24 36 48 60 72 84 96 Window Length (L) Window Length (L) Figure 7. CR index for the Exponential+Cosine series (case f). Table 3. Proper linkages based on the simulation results. Simulated Series Proper Linkages Exponential average, centroid, diana, mcquitty, median, single Sine average, centroid, mcquitty, median, single Linear + sine average, centroid, mcquitty, median, single Sine + Sine average, centroid, mcquitty, median, single Exponential Cosine average, single, mcquitty Exponential + Cosine average, centroid, single 4. Case Studies Here, let us apply the hierarchical clustering methods to identify adequate groups using real-world datasets. To this end, two time series datasets were considered as follows: 1. FORT series: Monthly Australian fortiﬁed wine sales (abbreviated to “FORT”) in thousands of liters from January 1980 to July 1995 with 187 observations [14,31]. This time series is part of the dataset AustralianWine from R package Rssa. Each of the seven variables of the full dataset contains 187 points. Since the data were missing values after June 1994, we used the ﬁrst 174 points. 2. Deaths series: Monthly accidental deaths in the USA from 1973 to 1978 including a total of 72 observations. This well-known time series dataset has been used by many authors and can be found in many time series books (as can be seen, for example, [32]). In this study, the USAccDeaths data of R package datasets were used. Figure 8 shows the time series plots of these datasets. It is evident that there is a seasonal structure in these time series. Furthermore, there is a downward linear trend in the FORT time series. CR CR AppliedMath 2021, 1 28 FORT series Deaths series 1980 1985 1990 1973 1974 1975 1976 1977 1978 1979 Figure 8. Time series plots of the FORT and Deaths series. The following steps provide a simple step-by-step procedure to group the elementary components of the time series by means of a hierarchical clustering method: Step 1: Choosing the window length One of the most important parameters in SSA is the window length (L). This parameter plays a pivotal role in SSA because the outputs of reconstruction and forecasting are affected by changing this parameter. There are some general recommendations for the choice of the window length L. It should be sufﬁciently large and the most detailed decomposition is achieved when L is close to the half of time series length (L ' N/2) [10]. Furthermore, in order to extract the periodic components of a time series with the known period P, the window lengths divisible by the period P provide better separability [14]. In the FORT time series, which is periodic with the period P = 12, L = 84 N 174 L 84 meets these recommendations, since 84 is close to = = 87 and = = 7. 2 2 P 12 Furthermore, the Deaths time series is periodic with the period P = 12. Therefore, N 72 L = 36 satisﬁes these recommendations, because 36 is equal to = and 2 2 L 36 = = 3. P 12 Step 2: Determining the number of clusters An important step in hierarchical clustering is the decision on where to cut the dendrogram. Similar to the simulation study proposed in Section 3, here, we use the number of clusters as a cutting rule of the dendrogram. If the purpose of time series analysis extracts the signal from noise, determining the number of clusters is quite straightforward; it is sufﬁcient to set the number of clusters to equal two. However, if we want to retrieve different components concealed in a time series such as the trend and periodic components, determining the number of clusters requires more information. In this case, we recommend using the w-correlation matrix owing to utilizing it to measure the distance matrix of hierarchical clustering. Figure 9 shows the matrix of absolute values of w-correlations between the 30 leading elementary reconstructed components of FORT series. The matrix of absolute values of w-correlations between the 36 elementary reconstructed components of Deaths series is depicted in Figure 10. In these ﬁgures, the white 1000 2000 3000 4000 5000 7000 8000 9000 10000 11,000 AppliedMath 2021, 1 29 color corresponds to zero and the black color corresponds to the absolute values equal to one. Highly correlated elementary reconstructed components can be easily found by looking at the w-correlation matrix and then we can place these components into the same cluster. It can be deduced from Figure 9 that the components of the FORT series can be partitioned into eight groups: f1g,f2, 3g,f4, 5g,f6, 7g,f8, 9g,f10, 11g,f12, 13g, f14, . . . , 84g. Furthermore, in order to separate signal from noise, the components can be split into two groups: signal (f1, . . . , 13g) and noise (f14, . . . , 84g). Using the information provided in Figure 10, the components of Deaths series can be partitioned into seven groups: f1g,f2, 3g,f4, 5g,f6g,f7, 8g,f9, 10g,f11, . . . , 36g. Additionally, in order to separate signal from noise, the components can be split into two groups: signal (f1, . . . , 10g) and noise (f11, . . . , 36g). Step 3: Selecting a proper linkage Although a proper linkage can be selected using the ﬁndings of the simulation study given in Section 3, we compared the different linkages based on the CR index for FORT and Deaths series. The outputs of clustering are reported in Tables 4 and 5, when it is assumed that the correct groups are those mentioned in Step 2. The results reported in these tables show that the single, median and centroid linkages can exactly identify the correct groups in FORT series (expressed in boldface). However, only the centroid linkage can exactly identify the correct groups in Deaths series (expressed in boldface). Based on the CR index reported in Tables 4 and 5, it can be concluded that the ward.D and ward.D2 linkages have the worst performance both in FORT and Deaths series. These ﬁndings are in good agreement with the simulation results obtained in Section 3. Figure 11 shows the dendrogram of the single linkage for our FORT series. The dendrogram of the centroid linkage for the Deaths series is depicted in Figure 12. The reconstruction of the FORT series with the help of the ﬁrst 13 eigentriples is presented in Figure 13. Furthermore, Figure 14 shows the plot of the signal reconstruction of the Deaths series based on the ﬁrst 10 eigentriples. W−correlation matrix F20 F19 F18 F17 F16 F15 F14 F13 F12 F11 F10 F9 F8 F7 F6 F5 F4 F3 F2 F1 F1 F2 F3 F4 F5 F6 F7 F8 F9 F10F11F12F13F14F15F16F17F18F19F20 Figure 9. Plot of the w-correlation matrix for FORT series. AppliedMath 2021, 1 30 W−correlation matrix F20 F19 F18 F17 F16 F15 F14 F13 F12 F11 F10 F9 F8 F7 F6 F5 F4 F3 F2 F1 F1 F2 F3 F4 F5 F6 F7 F8 F9 F10F11F12F13F14F15F16F17F18F19F20 Figure 10. Plot of the w-correlation matrix for Deaths series. Table 4. Comparison of linkages for the FORT series. Linkage CR Clustering Output average 0.491 f1g,f2, 3g,f4, 5g,f6, 7g,f8, 9, 12, 13g,f10, 11g,f14, . . . , 30g,f31, . . . , 84g centroid 1.000 f1g,f2, 3g,f4, 5g,f6, 7g,f8, 9g,f10, 11g,f12, 13g,f14, . . . , 84g complete 0.280 f1g,f2, 3g,f4, . . . , 7g,f8, 9, 12, 13g,f10, 11, 15, 16, 17g, f14, 18, . . . , 22, 26, 27, 28, 31, . . . , 60g,f23, 24, 25, 29, 30g,f61, . . . , 84g diana 0.462 f1g,f2, 3g,f4, 5g,f6, 7g,f8, 9, 12, 13g,f10, 11g, f14, . . . , 30, 33, 34g,f31, 32, 35, . . . , 84g mcquitty 0.491 f1g,f2, 3g,f4, 5g,f6, 7g,f8, 9, 12, 13g,f10, 11g, f14, . . . , 30g,f31, . . . , 84g median 1.000 f1g,f2, 3g,f4, 5g,f6, 7g,f8, 9g,f10, 11g,f12, 13g,f14, . . . , 84g single 1.000 f1g,f2, 3g,f4, 5g,f6, 7g,f8, 9g,f10, 11g,f12, 13g,f14, . . . , 84g ward.D 0.028 f1, . . . , 13, 29, 30g,f14, . . . , 17g,f18, . . . , 28g,f31, . . . , 45, 48g f46, 47, 49, . . . , 55g,f56, . . . , 64g,f65, . . . , 71g,f72, . . . , 84g ward.D2 0.024 f1, . . . , 13, 29, 30g,f14, . . . , 17g,f18, . . . , 28g,f31, . . . , 37, 39, 40, 41, 44, 45, 48g f38, 42, 43, 47, 49, . . . , 55g,f46, 63, 65, . . . , 71g,f56, . . . , 62, 64g,f72, . . . , 84g AppliedMath 2021, 1 31 Table 5. Comparison of linkages for the Deaths series. Linkage CR Clustering Output average 0.389 f1g,f2, 3g,f4, 5g,f6, . . . , 10g,f11, . . . , 18g,f19, . . . , 30, 32, 33, 34g,f31, 35, 36g centroid 1.000 f1g,f2, 3g,f4, 5g,f6g,f7, 8g,f9, 10g,f11, . . . , 36g complete 0.158 f1g,f2, 3, 11, 14, 15, 17g,f4, 5, 19, 20, 21, 22, 24g,f6, . . . , 10g,f12, 13, 16, 18g, f23, 25, . . . , 34g,f35, 36g diana 0.353 f1g,f2, 3g,f4, 5g,f6, . . . , 10g,f11, . . . , 22g,f23, . . . , 30g, f32, 33, 34g,f31, 35, 36g mcquitty 0.389 f1g,f2, 3g,f4, 5g,f6, . . . , 10g,f11, . . . , 18g,f19, . . . , 30g, f32, 33, 34g,f31, 35, 36g median 0.761 f1g,f2, 3g,f4, 5, 11, . . . , 30, 32, . . . , 36g,f6g,f7, 8g,f9, 10g,f31g single 0.914 f1g,f2, 3g,f4, 5g,f6, 7, 8g,f9, 10g,f11, . . . , 30, 32, . . . , 36g,f31g ward.D 0.056 f1, 6, . . . , 10, 23, 25, 31, 35, 36g,f2, 3g,f4, 5g,f11, . . . , 18g f19, . . . , 22g,f24, 26 . . . , 30g,f32, 33, 34g ward.D2 0.111 f1, 4, 5, 23, 25, 31, 35, 36g,f2, 3g,f6, . . . , 10g,f11, . . . , 18g f19, . . . , 24g,f26, . . . , 30g,f32, 33, 34g Cluster Dendrogram Figure 11. Dendrogram of the single linkage for the FORT series. Height 0.0 0.2 0.4 0.6 0.8 1.0 67 AppliedMath 2021, 1 32 Cluster Dendrogram Figure 12. Dendrogram of the centroid linkage for the Deaths series. Original Reconstructed Residuals 1980 1985 1990 1995 Time Figure 13. Original, reconstructed and residual time series plot for the FORT series. −500 0 500 2000 4000 1000 3000 5000 Height 0.0 0.1 0.2 0.3 0.4 0.5 27 AppliedMath 2021, 1 33 Original Reconstructed Residuals 1973 1974 1975 1976 1977 1978 1979 Time Figure 14. Original, reconstructed and residual time series plot for the Deaths series. 5. Conclusions In this paper, we conducted a comparison study in order to ﬁnd a proper hierarchical clustering method to identify the appropriate groups at the grouping step of SSA. In general, the simulation results provided a clear indication that the ward.D and ward.D2 linkages could not detect meaningful groups. In addition, the choice of the best hierarchical clustering linkage is not straightforward. It depends on the structure of a time series and the level of noise that disturbs a time series. In general, the evidence from this investigation suggests using the single, centroid, average and mcquitty linkages to group eigentriples in the grouping step of SSA. It should be noted that our study has a clear limitation. It is due to the fact that in the automatic identiﬁcation of SSA components, it should be assumed that the components to be identiﬁed are (approximately) separated from the rest of the time series. Author Contributions: Conceptualization, H.H., M.K. and C.B.; methodology, H.H., M.K. and C.B.; software, M.K.; validation, H.H., M.K. and C.B.; formal; investigation, H.H., M.K. and C.B. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Conﬂicts of Interest: The authors declare no conﬂict of interest. Appendix A The following R codes can be used to achieve the ﬁgures and results obtained in Section 4: #Loading the ’Rssa’ package library(Rssa) #-------------------------------------------------------- #Extracting the FORT series from ’AustralianWine’ dataset data("AustralianWine", package = "Rssa") wine <- window(AustralianWine, end = time(AustralianWine)[174]) −400 0 200 400 700080009000 11000 7000 9000 11000 AppliedMath 2021, 1 34 fort <- wine[, "Fortified"] #-------------------------------------------------------- #Extracting the Deaths series from ’datasets’ package data("USAccDeaths", package = "datasets") #-------------------------------------------------------- #Time series plots (Figure 8) layout(matrix(1:2, ncol = 2)) par(mar = c(2, 2, 2, 2)) plot.ts(fort, ylab = ’’, xlab = ’’, main = ’FORT series’) par(mar = c(2, 2, 2, 2)) plot.ts(USAccDeaths, ylab = ’’, xlab = ’’, main = ’Deaths series’) #-------------------------------------------------------- #Decomposition Stage s.fort <- ssa(fort, L = 84, neig = 84) s.deaths <- ssa(USAccDeaths, L = 36) #-------------------------------------------------------- #Plot of w-correlation matrix (Figures 9 and 10) plot(s.fort, type = "wcor", groups = 1:30, grid = 14, lty = 2) plot(s.deaths, type = "wcor", grid = 11, lty = 2) #-------------------------------------------------------- #This function returns w-correlation matrix. W.corr <- function(ssa.object, groups){ W <- wcor(ssa.object, groups = groups) W <- unclass(W) rownames(W) <- colnames(W) <- groups return(W) #-------------------------------------------------------- #Distance matrix for FORT and Deaths series Diss.fort <- 1 - abs(W.corr(ssa.object = s.fort, groups = 1:84)) Diss.deaths <- 1 - abs(W.corr(ssa.object = s.deaths, groups = 1:36)) #-------------------------------------------------------- #Clustering by a specified linkage, for example, ’single’ cluster.fort <- hclust(as.dist(Diss.fort), method = "single") split(1:84, cutree(cluster.fort, k = 8)) cluster.deaths <- hclust(as.dist(Diss.deaths), method = "centroid") split(1:36, cutree(cluster.deaths, k = 7)) #-------------------------------------------------------- AppliedMath 2021, 1 35 #Dendrogram (Figures 11 and 12) plot(cluster.fort, xlab = "") plot(cluster.deaths, xlab = "") #-------------------------------------------------------- #NOTE: In order to cluster by ’diana’ linkage, apply the following codes: library(cluster) cluster.fort <- diana(as.dist(Diss.fort), diss = TRUE) split(1:84, cutree(cluster.fort, k = 8)) cluster.deaths <- diana(as.dist(Diss.deaths), diss = TRUE) split(1:36, cutree(cluster.deaths, k = 7)) #-------------------------------------------------------- #Figures 13 and 14 plot(reconstruct(s.fort, groups = list(Reconstructed = 1:13)), add.residuals = T, plot.method = "xyplot", superpose = F, col = "black", main = "") plot(reconstruct(s.deaths, groups = list(Reconstructed = 1:10)), add.residuals = T, plot.method = "xyplot", superpose = F, col = "black", main = "") References 1. Atikur Rahman Khan, M.; Poskitt, D.S. Forecasting stochastic processes using singular spectrum analysis: Aspects of the theory and application. Int. J. Forecast. 2017, 33, 199–213. [CrossRef] 2. Arteche, J.; Garcia-Enriquez, J. Singular Spectrum Analysis for signal extraction in Stochastic Volatility models. Econom. Stat. 2017, 1, 85–98. [CrossRef] 3. Hassani, H.; Yeganegi, M.R.; Silva, E.S. A New Signal Processing Approach for Discrimination of EEG Recordings. Stats 2018, 1, 155–168. [CrossRef] 4. Saﬁ, S.M.M; Mohammad Pooyan, M.; Nasrabadi, A.M. Improving the performance of the SSVEP-based BCI system using optimized singular spectrum analysis (OSSA). Biomed. Signal Process. Control. 2018, 46, 46–58. [CrossRef] 5. Mahmoudvand, R.; Rodrigues, P.C. Predicting the Brexit Outcome Using Singular Spectrum Analysis. J. Comput. Stat. Model. 2018, 1, 9–15. 6. Lahmiri, S. Minute-ahead stock price forecasting based on singular spectrum analysis and support vector regression. Appl. Math. Comput. 2018, 320, 444–451. [CrossRef] 7. Saayman, A.; Klerk, J. Forecasting tourist arrivals using multivariate singular spectrum analysis. Tour. Econ. 2019, 25, 330–354. [CrossRef] 8. Hassani, H; Rua, A.; Silva E. S.; Thomakos, D. Monthly forecasting of GDP with mixed-frequency multivariate singular spectrum analysis. Int. J. Forecast. 2019, 35, 1263–1272. [CrossRef] 9. Poskitt, D.S. On Singular Spectrum Analysis and Stepwise Time Series Reconstruction. J. Time Ser. Anal. 2020, 41, 67–94. [CrossRef] 10. Golyandina, N.; Nekrutkin, V.; Zhigljavsky, A. Analysis of Time Series Structure: SSA and Related Techniques; Chapman & Hall/CRC: Boca Raton, FL, USA, 2001. 11. Golyandina, N.; Zhigljavsky, A. Singular Spectrum Analysis for Time Series, 2nd ed.; Springer Briefs in Statistics; Springer: Berlin/Heidelberg, Germany, 2020. 12. Sanei, S.; Hassani, H. Singular Spectrum Analysis of Biomedical Signals; Taylor & Francis/CRC: Boca Raton, FL, USA, 2016. 13. Hassani, H.; Mahmoudvand, R. Singular Spectrum Analysis Using R; Palgrave Pivot: London, UK, 2018. 14. Golyandina, N.; Korobeynikov, A.; Zhigljavsky, A. Singular Spectrum Analysis with R; Springer: Berlin/Heidelberg, Germany, 2018. 15. Golyandina, N. Particularities and commonalities of singular spectrum analysis as a method of time series analysis and signal processing. WIREs Comput. Stat. 2020, 12, e1487. [CrossRef] 16. Bilancia, M.; Campobasso, F. Airborne particulate matter and adverse health events: Robust estimation of timescale effects. In Classiﬁcation as a Tool for Research; Locarek-Junge, H., Weihs, C., Eds.; Springer: Berlin/Heidelberg, Germany, 2010; pp. 481–489. AppliedMath 2021, 1 36 17. Korobeynikov, A. Computation- and space-efﬁcient implementation of SSA. Stat. Its Interface 2010, 3, 257–368. [CrossRef] 18. Golyandina, N.; Korobeynikov, A. Basic Singular Spectrum Analysis and forecasting with R. Comput. Stat. Data Anal. 2014, 71, 934–954. [CrossRef] 19. Golyandina, N., Korobeynikov, A., Shlemov, A.; Usevich, K. Multivariate and 2D Extensions of Singular Spectrum Analysis with the Rssa Package. J. Stat. Softw. 2015, 67, 1–78. [CrossRef] 20. Johnson, R.A.; Wichern, D.W. Applied Multivariate Statistical Analysis, 6th ed.; Pearson Education Ltd: London, UK, 2013. 21. Kaufman, L.; Rousseeuw, P.J. Finding Groups in Data: An Introduction to Cluster Analysis; Wiley: New York, NY, USA, 1990. 22. Maechler, M.; Rousseeuw, P.; Struyf, A.; Hubert, M.; Hornik, K. Cluster: Cluster Analysis Basics and Extensions. R Package Version 2021, 2, 56. 23. R Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2021. Available online: https://www.R-project.org/ (accessed on 23 October 2021). 24. Charrad, M., Ghazzali, N., Boiteau, V.; Niknafs, A. NbClust: An R Package for Determining the Relevant Number of Clusters in a Data Set. J. Stat. Softw. 2014, 61, 1–36. [CrossRef] 25. Gordon, A.D. Classiﬁcation, 2nd ed.; Chapman & Hall: Boca Raton, FL, USA, 1999. 26. Contreras, P.; Murtagh, F. Hierarchical Clustering. In Handbook of Cluster Analysis; Henning, C., Meila, M., Murtagh, F., Rocci, R., Eds.; Chapman & Hall/CRC: Boca Raton, FL, USA, 2016; pp. 103–123. 27. Hennig, C. fpc: Flexible Procedures for Clustering. R Package Version 2.2-9. 2020. Available online: https://CRAN.R-project.org/ package=fpc. (accessed on 15 September 2021). 28. Hubert, L.; Arabie, P. Comparing partitions. J. Classif. 1985, 2, 193–218. [CrossRef] 29. Gates, A.J.; Ahn, Y.Y. The impact of random models on clustering similarity. J. Mach. Learn. Res. 2017, 18, 1–28. 30. Vinh, N.X.; Epps, J.; Bailey, J. Information theoretic measures for clusterings comparison: Variants, properties, normalization and correction for chance. J. Mach. Learn. Res. 2010, 11, 2837–2854. 31. Hyndman, R.J. Time Series Data Library. Available online: http://data.is/TSDLdemo (accessed on 10 May 2021). 32. Brockwell, P.J.; Davis, R.A. Introduction to Time Series and Forecasting, 2nd ed.; Springer: Berlin/Heidelberg, Germany, 2002.
AppliedMath – Multidisciplinary Digital Publishing Institute
Published: Dec 14, 2021
Keywords: time series; basic SSA; hierarchical clustering; corrected Rand index
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