Multivariate Time Series Similarity Searching

Multivariate time series (MTS) datasets are very common in various financial, multimedia, and hydrological fields. In this paper, a dimension-combination method is proposed to search similar sequences for MTS. Firstly, the similarity of single-dimension series is calculated; then the overall similarity of the MTS is obtained by synthesizing each of the single-dimension similarity based on weighted BORDA voting method. The dimension-combination method could use the existing similarity searching method. Several experiments, which used the classification accuracy as a measure, were performed on six datasets from the UCI KDD Archive to validate the method. The results show the advantage of the approach compared to the traditional similarity measures, such as Euclidean distance (ED), cynamic time warping (DTW), point distribution (PD), PCA similarity factorSPCA, and extended Frobenius norm (Eros), for MTS datasets in some ways. Our experiments also demonstrate that no measure can fit all datasets, and the proposed measure is a choice for similarity searches.

Download Full-text

Multivariate Time Series Link Prediction for Evolving Heterogeneous Network

International Journal of Information Technology & Decision Making ◽

10.1142/s0219622018500530 ◽

2019 ◽

Vol 18 (01) ◽

pp. 241-286 ◽

Cited By ~ 8

Author(s):

Alper Ozcan ◽

Sule Gunduz Oguducu

Keyword(s):

Time Series ◽

Real World ◽

Link Prediction ◽

Multivariate Time Series ◽

Similarity Measures ◽

Dynamic Structure ◽

Social Bookmarking ◽

Node Connectivity ◽

Worldwide Web ◽

Heterogeneous Social Networks

Link prediction is considered as one of the key tasks in various data mining applications for recommendation systems, bioinformatics, security and worldwide web. The majority of previous works in link prediction mainly focus on the homogeneous networks which only consider one type of node and link. However, real-world networks have heterogeneous interactions and complicated dynamic structure, which make link prediction a more challenging task. In this paper, we have studied the problem of link prediction in the dynamic, undirected, weighted/unweighted, heterogeneous social networks which are composed of multiple types of nodes and links that change over time. We propose a novel method, called Multivariate Time Series Link Prediction for evolving heterogeneous networks that incorporate (1) temporal evolution of the network; (2) correlations between link evolution and multi-typed relationships; (3) local and global similarity measures; and (4) node connectivity information. Our proposed method and the previously proposed time series methods are evaluated experimentally on a real-world bibliographic network (DBLP) and a social bookmarking network (Delicious). Experimental results show that the proposed method outperforms the previous methods in terms of AUC measures in different test cases.

Download Full-text

Pattern Matching Method Based on Point Distribution for Multivariate Time Series

Journal of Software ◽

10.3724/sp.j.1001.2009.00067 ◽

2009 ◽

Vol 20 (1) ◽

pp. 67-79 ◽

Cited By ~ 8

Author(s):

He-Shan GUAN ◽

Qing-Shan JIANG ◽

Sheng-Rui WANG

Keyword(s):

Time Series ◽

Pattern Matching ◽

Multivariate Time Series ◽

Matching Method ◽

Point Distribution

Download Full-text

A New Fuzzy Logic-Based Similarity Measure Applied to Large Gap Imputation for Uncorrelated Multivariate Time Series

Applied Computational Intelligence and Soft Computing ◽

10.1155/2018/9095683 ◽

2018 ◽

Vol 2018 ◽

pp. 1-15 ◽

Cited By ~ 1

Author(s):

Thi-Thu-Hong Phan ◽

André Bigand ◽

Émilie Poisson Caillault

Keyword(s):

Time Series ◽

Fuzzy Logic ◽

Similarity Measure ◽

Missing Values ◽

Multivariate Time Series ◽

Similarity Measures ◽

New Approach ◽

The One ◽

High Level ◽

Basic Similarity

The completion of missing values is a prevalent problem in many domains of pattern recognition and signal processing. Analyzing data with incompleteness may lead to a loss of power and unreliable results, especially for large missing subsequence(s). Therefore, this paper aims to introduce a new approach for filling successive missing values in low/uncorrelated multivariate time series which allows managing a high level of uncertainty. In this way, we propose using a novel fuzzy weighting-based similarity measure. The proposed method involves three main steps. Firstly, for each incomplete signal, the data before a gap and the data after this gap are considered as two separated reference time series with their respective query windowsQbandQa. We then find the most similar subsequence (Qbs) to the subsequence before this gapQband the most similar one (Qas) to the subsequence after the gapQa. To find these similar windows, we build a new similarity measure based on fuzzy grades of basic similarity measures and on fuzzy logic rules. Finally, we fill in the gap with average values of the window followingQbsand the one precedingQas. The experimental results have demonstrated that the proposed approach outperforms the state-of-the-art methods in case of multivariate time series having low/noncorrelated data but effective information on each signal.

Download Full-text