Algorithmic method of analysis of time series data for definition of prognostic parameters of engine fault

2011 ◽  
Author(s):  
Alexandr Kirillov ◽  
Olga Kirillova ◽  
Sergey Kirillov
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Prognostic Parameters ◽  
Algorithmic Method ◽  
Definition Of ◽  
Analysis Of Time Series

Efficient Time Series Clustering and Its Application to Social Network Mining

2014 ◽  
Vol 23 (2) ◽  
pp. 213-229 ◽  
Author(s):  
Cangqi Zhou ◽  
Qianchuan Zhao
Keyword(s):  
Time Series ◽  
Social Network ◽  
Information Diffusion ◽  
Time Series Data ◽  
Online Social Network ◽  
Dissimilarity Measure ◽  
Series Data ◽  
Formation Mechanisms ◽  
Network Analyses ◽  
Definition Of

AbstractMining time series data is of great significance in various areas. To efficiently find representative patterns in these data, this article focuses on the definition of a valid dissimilarity measure and the acceleration of partitioning clustering, a common group of techniques used to discover typical shapes of time series. Dissimilarity measure is a crucial component in clustering. It is required, by some particular applications, to be invariant to specific transformations. The rationale for using the angle between two time series to define a dissimilarity is analyzed. Moreover, our proposed measure satisfies the triangle inequality with specific restrictions. This property can be employed to accelerate clustering. An integrated algorithm is proposed. The experiments show that angle-based dissimilarity captures the essence of time series patterns that are invariant to amplitude scaling. In addition, the accelerated algorithm outperforms the standard one as redundancies are pruned. Our approach has been applied to discover typical patterns of information diffusion in an online social network. Analyses revealed the formation mechanisms of different patterns.

Dimensionality Reduction for the Analysis of Time Series Data from Wind Turbines

2017 ◽  
pp. 317-339 ◽  
Author(s):  
Jochen Garcke ◽  
Rodrigo Iza-Teran ◽  
Marvin Marks ◽  
Mandar Pathare ◽  
Dirk Schollbach ◽  
...  
Keyword(s):  
Time Series ◽  
Dimensionality Reduction ◽  
Wind Turbines ◽  
Time Series Data ◽  
Series Data ◽  
Analysis Of Time Series

Italy's disappearing ‘red regions’: a longitudinal analysis

Modern Italy ◽  
2020 ◽  
Vol 25 (3) ◽  
pp. 279-297
Author(s):  
Bruno Bracalente ◽  
Davide Pellegrino ◽  
Antonio Forcina
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Extended Period ◽  
Minimal Change ◽  
Series Data ◽  
Regional Government ◽  
Left Wing ◽  
Recent Period ◽  
Left And Right ◽  
Analysis Of Time Series

Using an analysis of time series data over an extended period, this article describes the waning strength of the left-wing vote in Italy's ‘red regions’. By analysing changes to the provincial share of the vote for successive principal left-wing parties over the period 1953–2018, the degree of continuity in relation to the left's traditional territorial entrenchment is assessed. It becomes clear that after an extended period of minimal change, in more recent years there has been an increasing disruption of previous patterns. A thorough analysis of voter transitions during the 2001–19 period in Umbria, the first red region in which the left lost control of the regional government, shows that in this case the gradual weakening of the traditional left-wing ‘vote of belonging’ has experienced a dramatic acceleration during the more recent period. This has been expressed in a growing rate of abstention, vote-switching according to the type of electoral contest, and a marked propensity to vote for populist movements and parties on both the left and right.

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