Market Simulation nodes by Scientific Strategy for KNIME - Community Edition version 4.3.1.v202102181051 by Decision Ready, LLC
The Correlation Concatenation node is designed to take any number of Input Correlation Matrices and join them into a single Output Correlation Matrix. The user can specify the degree of Cross Correlation each Matrix will have with the others Matrices when they are joined.
Concatenating Correlation Matrices is useful when the Horizontal Differentiation of Features have been independently generated but some Correlation is known to exist between them. For example, if 'Style', 'Color', and 'Ambience' Features were independently generated, then the Correlation Concatenation node could join these three Features together with some Cross Correlation.
Often there is also a relationship between the Elements within each Matrix depending upon the position of the Element. For example, travelers who stay at a luxury hotel will typically appreciate every aspect of that luxury wherever it is found. Hence, travelers who value the best 'Room' are also more likely to value the best 'Entertainment' and the best 'Food'. The top Element found in each Matrix has more Cross Correlation than other Element combinations. Similarly, economy travelers who do not place a high value on a good 'Room' are also not likely to place a high value on 'Entertainment' and 'Food'.
Typically the Matrix:Matrix Correlation will be modest (less than 0.5). Large Matrix:Matrix Correlations will require the Output Correlation Matrix to be repaired (see the 'Output Correlation Repaired Matrix' and the 'Output Correlation Error Matrix'). If large Feature Correlations are required then consider using the Differentiation Horizontal node instead.
All of the row and column names must be unique across all input tables otherwise the Matrices cannot be joined. If a specific 'Order' is not provided in the Input Matrix then the row index is used for matching Elements.
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