Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Multivariate time series (MTS) datasets are common in various multimedia, medical and financial applications. In order to efficiently perform k nearest neighbor searches for MTS datasets,  present a similarity measure, Eros (extended frobenius norm), an index structure, Muse (multilevel distance-based index structure for Eros), and a feature subset selection technique, Ropes (recursive feature elimination on common principal components for Eros). Eros is based on principal component analysis, and computes the similarity beten two MTS items by measuring how close the corresponding principal components are using the eigenvalues as ights. Muse constructs each level as a distance-based index structure without using the ights, up to z levels, which are combined at the query time with the ights. Ropes utilizes both the common principal components and the ights recursively in order to select a subset of features for Eros. The experimental results show the superiority of our techniques as compared to earlier approaches.
['Kiyoung Yang', 'Cyrus Shahabi']
I am writing a research paper on a idea An efficient k nearest neighbor search for multivariate time series
[{"from": "human", "value": "I am writing a research paper on a idea An efficient k nearest neighbor search for multivariate time series"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Multivariate time series (MTS) datasets are common in various multimedia, medical and financial applications. In order to efficiently perform k nearest neighbor searches for MTS datasets,  present a similarity measure, Eros (extended frobenius norm), an index structure, Muse (multilevel distance-based index structure for Eros), and a feature subset selection technique, Ropes (recursive feature elimination on common principal components for Eros). Eros is based on principal component analysis, and computes the similarity beten two MTS items by measuring how close the corresponding principal components are using the eigenvalues as ights. Muse constructs each level as a distance-based index structure without using the ights, up to z levels, which are combined at the query time with the ights. Ropes utilizes both the common principal components and the ights recursively in order to select a subset of features for Eros. The experimental results show the superiority of our techniques as compared to earlier approaches."}]