English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Similarity search on time series is a frequent operation in large-scaledata-driven applications. Sophisticated similarity measures are standard fortime series matching, as they are usually misaligned. Dynamic Time Warping orDTW is the most widely used similarity measure for time series because itcombines alignment and matching at the same time. However, the alignment makesDTW slow. To speed up the expensive similarity search with DTW, branch andbound based pruning strategies are adopted. However, branch and bound basedpruning are only useful for very short queries (low dimensional time series),and the bounds are quite weak for longer queries. Due to the loose boundsbranch and bound pruning strategy boils down to a brute-force search.  To circumvent this issue, we design SSH (Sketch, Shingle, & Hashing), anefficient and approximate hashing scheme which is much faster than thestate-of-the-art branch and bound searching technique: the UCR suite. SSH usesa novel combination of sketching, shingling and hashing techniques to produce(probabilistic) indexes which align (near perfectly) with DTW similaritymeasure. The generated indexes are then used to create hash buckets forsub-linear search. Our results show that SSH is very effective for longer timesequence and prunes around 95% candidates, leading to the massive speedup insearch with DTW. Empirical results on two large-scale benchmark time seriesdata show that our proposed method can be around 20 times faster than thestate-of-the-art package (UCR suite) without any significant loss in accuracy.

SSH (Sketch, Shingle, & Hash) for Indexing Massive-Scale Time Series