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It has two\ncategories according to various number of the variables, they are univariate time series (UTS) and multivariate time\nseries(MTS).\n\nMTS (Multivariate time series) is an important type of data that\nis indispensable in a variety of domains as  medicine \ndomain which is the evolution of a group of synchronous\nvariables over a duration of time as shown in the figure and\nthere is a lot of effort given due to the expensive of gathering \nthese labeled data to be able to offer a method gives a reliable \naccuracy by only using a limited amount of these data.\n\n![alt text](https://cdn.analyticsvidhya.com/wp-content/uploads/2018/09/mts.jpg)\n\nWhat's PCA?\n\nThis is a linear unsupervised algorithm to find orthogonal transformation axes that diagonalize the covariance \nmatrix the goal is to eliminate low variance and highly correlated features.\n\nWhy PCA?\n\nDue to the high\ndimensionality of MTS, the dimensionality reduction is proposed to validly integrate into the clustering,classification and regression process and a good MTS accuracy can be obtained in lower dimensions\n\nSuppose there was a dataset X having N multivariate time series\nΣ_i=cov⁡(x_i)  , x_i ∈ R^(n_i * m) where  n_i is the length of MTS sample and m is the number of the variables\nΣ = 1/N ∑Σ_i ","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsherif-mooo%2Fmts-cpca","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsherif-mooo%2Fmts-cpca","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsherif-mooo%2Fmts-cpca/lists"}