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Efficient mining of understandable patterns from multivariate interval time series
2007
Data mining and knowledge discovery
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We present a new method for the understandable description of local temporal relationships in multivariate data, called Time Series Knowledge Mining (TSKM). ...
We define the Time Series Knowledge Representation (TSKR) as a new language for expressing temporal knowledge in time interval data. ...
The first author was partly supported by Siemens Corporate Research, Princeton, NJ during this time. ...
doi:10.1007/s10618-007-0070-1
fatcat:j2ijrwv6tnag3gzeqypuhyn36i
Unsupervised pattern mining from symbolic temporal data
2007
SIGKDD Explorations
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For time points, sequential pattern mining algorithms can be used to express equality and order of time points with gaps in multivariate data. ...
For univariate data and limited gaps suffix tree methods are more efficient. Recently, efficient algorithms have been proposed to mine the more general concept of partial order from time points. ...
First a multivariate symbolic interval series is obtained from numeric time series. Event patterns describe several more or less simultaneous intervals and express the concept of synchronicity. ...
doi:10.1145/1294301.1294302
fatcat:rwcvkifhknh2reo6zp2tkco4vq
Visualizing frequent patterns in large multivariate time series
2011
Visualization and Data Analysis 2011
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., the most efficient or least efficient motif) can be quickly detected from a large time series for further investigation. ...
To find frequent motifs, we use several temporal data mining and event encoding techniques to cluster and convert a multivariate time series to a sequence of events. ...
In general, we use the above methods to map a multivariate time series to frequent patterns. ...
doi:10.1117/12.872169
dblp:conf/vda/HaoMJSKDPR11
fatcat:hwhmc6m44vdq3cuevbcjiany7y
Visual exploration of frequent patterns in multivariate time series
2012
Information Visualization
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Since a large time series can contain hundreds of motifs, there is a need to support interactive analysis and exploration. ...
The detection of frequently occurring patterns, also called motifs, in data streams has been recognized as an important task. ...
Pattern Finding in Large Multivariate Time Series A schematic overview of our approach is provided in Figure 2 . ...
doi:10.1177/1473871611430769
fatcat:xsxorbfbg5fn3iijpmu6kks7z4
Discovery of Temporal Association Rules in Multivariate Time Series
2018
DEStech Transactions on Computer Science and Engineering
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This paper reviews some methods for temporal association rule mining, and proposes two similar algorithms for the mining of frequent patterns in single and multivariate time series, both scalable and efficient ...
This paper focuses on mining association rules in multivariate time series. ...
The whole process includes time series representation, pattern mining in single timer series and clustering, pattern mining in multivariate time series and temporal association rule generation. ...
doi:10.12783/dtcse/mmsta2017/19653
fatcat:qsjp2fc56vav7gdclrz234iq6i
Handling temporality of clinical events with application to Adverse Drug Event detection in Electronic Health Records: A scoping review
[article]
2019
arXiv
pre-print
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According to the literature retrieved the main methods were found to fall into 5 different approaches: based on temporal abstraction, graph-based, learning weights and data tables containing time series ...
Based on a review of the existing literature, 11 articles from the last 10 years were chosen to be studied. ...
Converting the data from time-stamped to a series of uniform time intervals can benefit the pattern mining process. [8] Both studies by Moskovitch et al. ...
arXiv:1904.04940v1
fatcat:hce6dhg3g5dl5j77nighkz5wpq
Procedural Steps for Knowledge Mining in Time Series
2013
International Journal of Computer Applications
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The Hierarchical Time series Knowledge Representation (HTKR) is the hierarchical language which expresses the temporal aspects of coincidence and partial order, for interval patterns. ...
We present mining procedural steps which are more efficient, effective and based on item set techniques. ...
Allen's relations are not advantageous for pattern discovery from interval time series, as shown by the following examples. ...
doi:10.5120/10525-5508
fatcat:74slcxik6jbnzk6zzkw7nylp4m
Classification of multivariate time series via temporal abstraction and time intervals mining
2014
Knowledge and Information Systems
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data points into a series of symbolic time intervals; (2) mining these intervals to discover frequent temporal patterns, using Allen's 13 temporal relations; (3) using the patterns as features to induce ...
Classification of multivariate time series data, often including both time points and intervals at variable frequencies, is a challenging task. ...
For insightful discussions time intervals mining and classification using TIRPs our thanks to Christos Faloutsos, Christian Freksa, Fabian Moerchen, Dhaval Patel and Iyad Batel. ...
doi:10.1007/s10115-014-0784-5
fatcat:u4lp5ywtubgk3flhbcyfi2ry4m
Classification-driven temporal discretization of multivariate time series
2014
Data mining and knowledge discovery
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Increasingly, temporal abstraction, in which a series of raw-data time points is abstracted into a set of symbolic time intervals, is being used for classification of multivariate time series. ...
data points into a series of symbolic time intervals (based on either unsupervised or supervised temporal abstraction); (2) mining these time intervals to discover frequent temporal-interval relation ...
For insightful discussions of time intervals mining and classification using TIRPs, we wish to express our thanks to Christos Faloutsos, Christian Freksa, Fabian Moerchen, Dhaval Patel, and Iyad Batel. ...
doi:10.1007/s10618-014-0380-z
fatcat:uobcfmyub5eyfh62gksnsawsci
Mining Temporal Exception Rules from Multivariate Time Series Using a new Support Measure
2020
Journal of Information and Data Management
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We performed an extensive experimental analysis in real multivariate time series to verify the practical applicability of TRiER. ...
We also present a new support measure to manipulate time series. This measure considers the context in which a pattern occurs, thus incorporating more semantics to the results obtained. ...
BACKGROUND
Time Series Time series is a sequence of time-ordered observations with regular time intervals between each pair of observations [Mitsa 2010 ]. ...
doi:10.5753/jidm.2020.2020
fatcat:vch2uopsrrhgpalgbm5m5ektsa
Optimized Incremental Mining of Customer Buying Behavior using Temporal Association Rules
2019
VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE
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To mine customers' behavior in a time-variant database, the re-mining of the updated database is required that further increases processing cost in terms of execution time and memory space with every update ...
In this paper, an optimized incremental technique is proposed that utilizes temporal association rule mining in a time-variant database for mining customer behavioral patterns in an updated database. ...
Multivariate Time Series Definition 4 (Multivariate Time Series): A multivariate time series (MTS) [32] comprises of individual time series, denoted by (𝑇𝑆 1 , 2 ,𝑇𝑆 3 ,…,𝑇𝑆 k ). ...
doi:10.35940/ijitee.l2482.1081219
fatcat:evnbvsqiubhzxodqnfxmxldo4i
A Pattern Mining Approach for Classifying Multivariate Temporal Data
2011
2011 IEEE International Conference on Bioinformatics and Biomedicine
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We study the problem of learning classification models from complex multivariate temporal data encountered in electronic health record systems. ...
Temporal pattern mining usually returns a large number of temporal patterns, most of which may be irrelevant to the classification task. ...
The problem of mining temporal patterns from time interval data is a relatively young research field. ...
doi:10.1109/bibm.2011.39
pmid:22267987
pmcid:PMC3261774
dblp:conf/bibm/BatalVCH11
fatcat:ix3sdpiwjngorczsi2mvhirzpa
Fast time intervals mining using the transitivity of temporal relations
2013
Knowledge and Information Systems
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The mined symbolic time intervals can be part of the input, or can be generated by a temporalabstraction process from raw time-stamped data. ...
Our experimental comparison of the KarmaLego algorithm's runtime performance to several existing state of the art time intervals pattern mining methods demonstrated a significant speed up, especially with ...
intervals mining. ...
doi:10.1007/s10115-013-0707-x
fatcat:gmau266rrjdvxb5h7kpj45sixq
Mining Signatures from Event Sequences
2015
IJARCCE
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The framework allows the presentation, extra4ction, and mining of high order latent occasion event structure and relationships between single and many sequences. ...
This paper proposes a novel secular knowledge representation and learning framework to proposed largescale secular signature mining of longitudinal heterogeneous occasional data. ...
[14] , [16] , [15] proposed a novel Time Series Knowledge Representation (TSKR) as a pattern language (grammar) for temporal knowledge discovery from multivariate time series and symbolic interval ...
doi:10.17148/ijarcce.2015.44129
fatcat:up6onqghvje7rgj6bi4o4aw7ky
An efficient pattern mining approach for event detection in multivariate temporal data
2015
Knowledge and Information Systems
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pattern mining is a special case of time-interval pattern mining, in which all intervals are simply time points with zero durations. ...
This approach first converts the time series data into time-interval sequences of temporal abstractions. ...
We presented an efficient algorithm that mines time-interval patterns backward in time, starting from patterns related to most recent observations. ...
doi:10.1007/s10115-015-0819-6
pmid:26752800
pmcid:PMC4704806
fatcat:7pelhltbpzbeziaptxekgzol5q
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