DeepSense: a unified deep learning framework for time-series mobile sensing data processing

DeepSense: a unified deep learning framework for time-series mobile sensing data processing Yao et al., WWW'17 DeepSense is a deep learning framework that runs on mobile devices, and can be used for regression and classification tasks based on data coming from mobile sensors (e.g., motion sensors). An example of a classification task is heterogeneous human ... Continue Reading

Chronix: Long term storage and retrieval technology for anomaly detection in operational data

Chronix: Long term storage and retrieval technology for anomaly detection in operational data Lautenschlager et al., FAST 2017 Chronix (http://www.chronix.io/ ) is a time-series database optimised to support anomaly detection. It supports a multi-dimensional generic time series data model and has built-in high level functions for time series operations. Chronix also a scheme called "Date-Delta-Compaction" (DDC) ... Continue Reading

Dynamic Time Warping averaging of time series allows faster and more accurate classification

Dynamic Time Warping averaging of time series allows faster and more accurate classification - Petitjean et al. ICDM 2014 For most time series classification problems, using the Nearest Neighbour algorithm (find the nearest neighbour within the training set to the query) is the technique of choice. Moreover, when determining the distance to neighbours, we want ... Continue Reading

Searching and mining trillions of time series subsequences under Dynamic Time Warping

Searching and mining trillions of time series subsequences under dynamic time warping - Rakthanmanon et al. SIGKDD 2012 What an astonishing paper this is! By 2012, Dynamic Time Warping had been shown to be the time series similarity measure that generally performs the best for matching, but because of its computational complexity researchers and practitioners ... Continue Reading

Towards parameter-free data mining

Towards Parameter-Free Data Mining - Keogh et al. SIGKDD 2004 Another time series paper today from the Facebook Gorilla references. Keogh et al. describe an incredibly simple and easy to implement scheme that does surprisingly well with clustering, anomaly detection, and classification tasks over time series data. As per the title of the paper, it ... Continue Reading