SSH (Sketch, Shingle, & Hash) for Indexing Massive-Scale Time Series
Chen Luo, Anshumali Shrivastava

TL;DR
This paper introduces SSH, a novel hashing-based indexing method that significantly accelerates similarity search in large-scale time series data using DTW, achieving up to 20x speedup with minimal accuracy loss.
Contribution
The paper presents SSH, a new probabilistic hashing scheme that improves the speed of DTW-based similarity search for long time series, outperforming existing branch and bound methods.
Findings
SSH prunes around 95% of candidates in search.
SSH is approximately 20 times faster than the UCR suite.
SSH maintains comparable accuracy to state-of-the-art methods.
Abstract
Similarity search on time series is a frequent operation in large-scale data-driven applications. Sophisticated similarity measures are standard for time series matching, as they are usually misaligned. Dynamic Time Warping or DTW is the most widely used similarity measure for time series because it combines alignment and matching at the same time. However, the alignment makes DTW slow. To speed up the expensive similarity search with DTW, branch and bound based pruning strategies are adopted. However, branch and bound based pruning are only useful for very short queries (low dimensional time series), and the bounds are quite weak for longer queries. Due to the loose bounds branch and bound pruning strategy boils down to a brute-force search. To circumvent this issue, we design SSH (Sketch, Shingle, & Hashing), an efficient and approximate hashing scheme which is much faster than the…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsTime Series Analysis and Forecasting · Data Mining Algorithms and Applications
MethodsPruning · SPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings · Dynamic Time Warping
