A new correlation coefficient for bivariate time-series data

Erdem, Orhan; Ceyhan, Elvan; Varlı, Yusuf

doi:10.1016/j.physa.2014.07.054

Cited by 35 publications

(8 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For that, we compute a weight w t ′ q for each candidate word t ′ q . We propose to estimate this weight from the timeseries for N i t and N i t ′ q with the correlation coefficient proposed in [18]. This coefficient, primarily designed to analyze stock prices, has two desirable properties for our application: (i) it is parameter-free and (ii) there is no stationarity assumption for the validity of this coefficient, contrary to common coefficients, e.g.…”

Section: Selection Of Words Describing Eventsmentioning

confidence: 99%

“…This practically corresponds to the first order auto-correlation of the time-series for N i t and N i t ′ q . The proof that ρ O satisfies |ρ O | 1 using the Cauchy-Schwartz inequality appears in [18]. Eventually, we define the weight of the term t ′ q as an affine function of ρ O to conform with our definition of bursty topic, i.e.…”

Section: Selection Of Words Describing Eventsmentioning

confidence: 99%

See 1 more Smart Citation

Mention-anomaly-based Event Detection and tracking in Twitter

Guille

Favre

2014

2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014)

View full text Add to dashboard Cite

Section: Selection Of Words Describing Eventsmentioning

confidence: 99%

Section: Selection Of Words Describing Eventsmentioning

confidence: 99%

Mention-anomaly-based Event Detection and tracking in Twitter

Guille

Favre

2014

2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014)

View full text Add to dashboard Cite

“…For that, we compute a weight w q for each candidate word t q . We propose to estimate this weight from the time-series for N i t and N i t q with the correlation coefficient proposed by Erdem et al (2012). This coefficient, primarily designed to analyze stock prices, has two desirable properties for our application: (i) it is parameter-free and (ii) there is no stationarity assumption for the validity of this coefficient, contrary to common coefficients, e.g.…”

Section: Selection Of Words Describing Eventsmentioning

confidence: 99%

“…This practically corresponds to the first order auto-correlation of the time-series for N i t and N i t q . The proof that ρ O satisfies |ρ O | 1 using the Cauchy-Schwartz inequality is given by Erdem et al (2012). Eventually, we define the weight of the term t q as an affine function of ρ O to conform with our definition of bursty topic, i.e.…”

Section: Selection Of Words Describing Eventsmentioning

confidence: 99%

Event detection, tracking, and visualization in Twitter: a mention-anomaly-based approach

Guille

Favre

2015

Soc. Netw. Anal. Min.

View full text Add to dashboard Cite

The ever-growing number of people using Twitter makes it a valuable source of timely information. However, detecting events in Twitter is a difficult task, because tweets that report interesting events are overwhelmed by a large volume of tweets on unrelated topics. Existing methods focus on the textual content of tweets and ignore the social aspect of Twitter. In this paper, we propose mention-anomaly-based event detection (MABED), a novel statistical method that relies solely on tweets and leverages the creation frequency of dynamic links (i.e., mentions) that users insert in tweets to detect significant events and estimate the magnitude of their impact over the crowd. MABED also differs from the literature in that it dynamically estimates the period of time during which each event is discussed, rather than assuming a predefined fixed duration for all events. The experiments we conducted on both English and French Twitter data show that the mention-anomaly-based approach leads to more accurate event detection and improved robustness in presence of noisy Twitter content. Qualitatively speaking, we find that MABED helps with the interpretation of detected events by providing clear textual descriptions and precise temporal descriptions. We also show how MABED can help understanding users' interest. Furthermore, we describe three visualizations designed to favor an efficient exploration of the detected events.

show abstract

“…For related earlier work, see Box and Tiao (1977). As examples of more recent developments, Podobnik and Stanley (2008) proposed an approach called de-trended cross-correlation analysis for determining the correlation between two non-stationary time series and Erdem et al (2014) proposed a new correlation coefficient that takes into account the nonstationarity in the data.…”

Section: Introductionmentioning

confidence: 99%