# 10-Dimensional PCA Variation in % (Professional Only)

Abbreviation: PCA10%Var
Category: Principal Components Analysis (PCA)
Input Parameters:

 Name Range Default Time Series #1 Close Time Series #2 Close Time Series #3 Close Time Series #4 Close Time Series #5 Close Time Series #6 Close Time Series #7 Close Time Series #8 Close Time Series #9 Close Time Series #10 Close Window Size Int >= 10 50 Vector Number 1 <= Int <= 10 1

Calculation:

Performs the Principal Component Analysis of the last n points in the space of X1…X10, calculates the mth eigenvector and its eigenvalue expressed in % of total sum of all eigenvalues,

where

X1 = Time Series 1
X2 = Time Series 2
X3 = Time Series 3
X4 = Time Series 4
X5 = Time Series 5
X6 = Time Series 6
X7 = Time Series 7
X8 = Time Series 8
X9 = Time Series 9
X10 = Time Series 10
n = Window Size
m = Vector Number

Discussion:

Calculates the percent of total variation along the mth significant direction in the data. For more information on Principal Component Analysis refer to Principal Components Analysis (PCA) Discussion.