Name
Abstract | ||
|---|---|---|
Time-series data are often characterized by a large degree of self-similarity, which arises in application domains featuring periodicity or seasonality. While self-similarity has shown to be an effective prior for modeling real data in the signal and image-processing literature, it has received much less attention in time-series literature, where only few works leveraging the self-similarity for anomaly detection have been presented. Here we introduce a novel change-detection test to detect structural changes in time series by analyzing their self-similarity. The core of the proposed solution is the definition of a change indicator to quantitatively assesses the self-similarity of the time-series data over time. In particular, the change indicator is obtained by comparing each patch to be analyzed with its most similar counterpart in a change-free training set. Experimental results on the flow measurements in the water distribution network of the Barcelona city show the effectiveness of the proposed solution. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/IJCNN.2014.6889860 | Neural Networks |
Keywords | Field | DocType |
data analysis,time series,Barcelona city,change indicator,change-detection test,change-free training set,flow measurements,time series structural change detection,time-series data self-similarity,water distribution network | Training set,Time series data analysis,Data mining,Anomaly detection,Change detection,Pattern recognition,Computer science,Distribution networks,Seasonality,Artificial intelligence,Self-similarity | Conference |
ISSN | Citations | PageRank |
2161-4393 | 8 | 0.51 |
References | Authors | |
16 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Giacomo Boracchi | 1 | 324 | 30.49 |
| Manuel Roveri | 2 | 272 | 30.19 |