Time series similarity measures (tutorial PM-2)

2000 ◽  
Author(s):  
Dimitrios Gunopulos ◽  
Gautam Das
Keyword(s):  
Time Series ◽  
Similarity Measures

scholarly journals On-line Elastic Similarity Measures for time series

2019 ◽  
Vol 88 ◽  
pp. 506-517 ◽  
Author(s):  
Izaskun Oregi ◽  
Aritz Pérez ◽  
Javier Del Ser ◽  
Jose A. Lozano
Keyword(s):  
Time Series ◽  
Similarity Measures ◽  
On Line

Wheat planted area detection from the MODIS NDVI time series classification using the nearest neighbour method calculated by the Euclidean distance and cosine similarity measures

2019 ◽  
Vol 35 (13) ◽  
pp. 1400-1414 ◽  
Author(s):  
Miriam Rodrigues da Silva ◽  
Osmar Abílio de Carvalho ◽  
Renato Fontes Guimarães ◽  
Roberto Arnaldo Trancoso Gomes ◽  
Cristiano Rosa Silva
Keyword(s):  
Time Series ◽  
Euclidean Distance ◽  
Similarity Measures ◽  
Cosine Similarity ◽  
Nearest Neighbour ◽  
Time Series Classification ◽  
Modis Ndvi ◽  
Ndvi Time Series ◽  
Cosine Similarity Measures

Time Series Mining

2008 ◽  
pp. 14-43
Author(s):  
Wynne Hsu ◽  
Mong Li Lee ◽  
Junmei Wang
Keyword(s):  
Time Series ◽  
Dimension Reduction ◽  
Similarity Measures ◽  
Sequential Patterns ◽  
Sequence Mining ◽  
Periodic Patterns ◽  
Background Material ◽  
Time Series Mining ◽  
Data Discretization

In this chapter, we will first give the background and review existing works in time series mining. The background material will include commonly used similarity measures and techniques for dimension reduction and data discretization. Then we will examine techniques to discover periodic and sequential patterns. This will lay the groundwork for the subsequent three chapters on mining dense periodic patterns, incremental sequence mining, and mining progressive patterns.

Multivariate Time Series Link Prediction for Evolving Heterogeneous Network

2019 ◽  
Vol 18 (01) ◽  
pp. 241-286 ◽  
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.

Disentangling synchrony from serial dependency in complex climate networks: Comparing Event Synchronization and Event Coincidence Analysis

2020 ◽  
Author(s):  
Adrian Odenweller ◽  
Reik Donner
Keyword(s):  
Time Series ◽  
Extreme Events ◽  
Spatial Scales ◽  
Grid Point ◽  
Explanatory Power ◽  
Similarity Measures ◽  
Tropical Rainfall Measuring Mission ◽  
Real World Data ◽  
Serial Dependency ◽  
Climate Networks

<p>The quantification of synchronization phenomena of extreme events has recently aroused a great deal of interest in various disciplines. Climatological studies therefore commonly draw on spatially embedded climate networks in conjunction with nonlinear time series analysis. Among the multitude of similarity measures available to construct climate networks, Event Synchronization and Event Coincidence Analysis (ECA) stand out as two conceptually and computationally simple nonlinear methods. While ES defines synchrony in a data adaptive local way that does not distinguish between different time scales, ECA requires the selection of a specific time scale for synchrony detection.</p><p>Herein, we provide evidence that, due to its parameter-free structure, ES has structural difficulties to disentangle synchrony from serial dependency, whereas ECA is less prone to such biases. We use coupled autoregressive processes to numerically study the sensitivity of results from both methods to changes of coupling and autoregressive parameters. This reveals that ES has difficulties to detect synchronies if events tend to occur temporally clustered, which can be expected from climate time series with extreme events exceeding certain percentiles.</p><p>These conceptual concerns are not only reproducible in numerical simulations, but also have implications for real world data. We construct a climate network from satellite-based precipitation data of the Tropical Rainfall Measuring Mission (TRMM) for the Indian Summer Monsoon, thereby reproducing results of previously published studies. We demonstrate that there is an undesirable link between the fraction of events on subsequent days and the degree density at each grid point of the climate network. This indicates that the explanatory power of ES climate networks might be hampered since trivial local properties of the underlying time series significantly predetermine the final network structure, which holds especially true for areas that had previously been reported as important for governing monsoon dynamics at large spatial scales. In contrast, ECA does not appear to be as vulnerable to these biases and additionally allows to trace the spatiotemporal propagation of synchrony in climate networks.</p><p>Our analysis rests on corrected versions of both methods that alleviate different normalization problems of the original definitions, which is especially important for short time series. Our finding suggest that careful event detection and diligent preprocessing is recommended when applying ES, while this is less crucial for ECA. Results obtained from ES climate networks therefore need to be interpreted with caution.</p>

Clustering Methodology for Time Series Mining

2009 ◽  
Vol 40 (1) ◽  
pp. 81-86
Author(s):  
Pēteris Grabusts ◽  
Arkady Borisov
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Clustering Algorithm ◽  
Time Series Data ◽  
Similarity Measures ◽  
Longest Common Subsequence ◽  
Series Data ◽  
Time Series Clustering ◽  
Series Analysis ◽  
Time Series Mining

Clustering Methodology for Time Series MiningA time series is a sequence of real data, representing the measurements of a real variable at time intervals. Time series analysis is a sufficiently well-known task; however, in recent years research has been carried out with the purpose to try to use clustering for the intentions of time series analysis. The main motivation for representing a time series in the form of clusters is to better represent the main characteristics of the data. The central goal of the present research paper was to investigate clustering methodology for time series data mining, to explore the facilities of time series similarity measures and to use them in the analysis of time series clustering results. More complicated similarity measures include Longest Common Subsequence method (LCSS). In this paper, two tasks have been completed. The first task was to define time series similarity measures. It has been established that LCSS method gives better results in the detection of time series similarity than the Euclidean distance. The second task was to explore the facilities of the classical k-means clustering algorithm in time series clustering. As a result of the experiment a conclusion has been drawn that the results of time series clustering with the help of k-means algorithm correspond to the results obtained with LCSS method, thus the clustering results of the specific time series are adequate.

Sign in / Sign up

Export Citation Format

Share Document