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

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

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

Time series classification under more realistic assumptions

Time series classification under more realistic assumptions - Hu et al. ICDM 2013 This paper sheds light on the gap between research results in time series classification, and what you're likely to see if you try to apply the results in the real world. And having identified the gap of course, the authors go on … Continue reading Time series classification under more realistic assumptions

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

Finding surprising patterns in a time series database in linear time and space

Finding Surprising Patterns in a Time Series Database in Linear Time and Space - Keogh et al. SIGKDD 2002 In the Facebook Gorilla paper, the authors mentioned a number of additional time series analysis techniques they'd like to add to the system over time. Today's paper is one of them, and it deals with the … Continue reading Finding surprising patterns in a time series database in linear time and space