An Efficient Hash-Based Method for Time Series Motif Discovery

Xuan, Pham Thanh; Anh, Duong Tuan

doi:10.1007/978-3-030-03014-8_17

Cited by 3 publications

(1 citation statement)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The discover function (line 6) applies a traditional motif discovery approach. More specifically, discover function applies an adapted hash-based approach [62] for exact match motif discovery [52] 1 . It checks all subsequences of size w in cs to discovery motifs of length w. The first step applies a hash function for registering the positions of each subsequence s i .…”

Section: Spatial-time Motif Discoverymentioning

confidence: 99%

Spatial-time motifs discovery

Borges

Dutra

Bazaz

et al. 2020

IDA

View full text Add to dashboard Cite

Discovering motifs in time series data has been widely explored. Various techniques have been developed to tackle this problem. However, when it comes to spatial-time series, a clear gap can be observed according to the literature review. This paper tackles such a gap by presenting an approach to discover and rank motifs in spatial-time series, denominated Combined Series Approach (CSA). CSA is based on partitioning the spatial-time series into blocks. Inside each block, subsequences of spatial-time series are combined in a way that hash-based motif discovery algorithm is applied. Motifs are validated according to both temporal and spatial constraints. Later, motifs are ranked according to their entropy, the number of occurrences, and the proximity of their occurrences. The approach was evaluated using both synthetic and seismic datasets. CSA outperforms traditional methods designed only for time series. CSA was also able to prioritize motifs that were meaningful both in the context of synthetic data and also according to seismic specialists.

show abstract

Section: Spatial-time Motif Discoverymentioning

confidence: 99%