Zoom-SVD: Fast and Memory Efficient Method for Extracting Key Patterns in an Arbitrary Time Range


Given multiple time series data, how can we efficiently find latent patterns in an arbitrary time range? Singular value decomposition (SVD) is a crucial tool to discover hidden factors in multiple time series data, and has been used in many data mining applications including dimensionality reduction, principal component analysis, recommender systems, etc. Along with its static version, incremental SVD has been used to deal with multiple semi-infinite time series data and to identify patterns of the data. However, existing SVD methods for the multiple time series data analysis do not provide functionality for detecting patterns of data in an arbitrary time range: standard SVD requires data for all intervals corresponding to a time range query, and incremental SVD does not consider an arbitrary time range.

In this paper, we propose Zoom-SVD, a fast and memory efficient method for finding latent factors of time series data in an arbitrary time range. Zoom-SVD incrementally compresses multiple time series data block by block to reduce the space cost in storage phase, and efficiently computes singular value decomposition (SVD) for a given time range query in query phase by carefully stitching stored SVD results. Through extensive experiments, we demonstrate that Zoom-SVD is up to 15x faster, and requires 15x less space than existing methods. Our case study shows that Zoom-SVD is useful for capturing past time ranges whose patterns are similar to a query time range.


Zoom-SVD is described in the following paper:



Name#Time length#AttributeDescriptionSourceLink
Activity monitoring382K41 PAMAP2 Physical Activity Monitoring Data Set UCI Machine Learning Repository Link
Gas sensor4.1M16 Gas sensor array under dynamic gas mixtures Data Set UCI Machine Learning Repository Link
London Air quality350K11 Measurement of London Air quality such as nitric oxide, temperature, and so on London Air