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
Tag: Time series
Analysing or storing time series data
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
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 Towards parameter-free data mining
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
BTrDB: Optimizing Storage System Design for Timeseries Processing
BTrDB: Optimizing Storage System Design for Timeseries Processing - Anderson & Culler 2016 It turns out you can accomplish quite a lot with 4,709 lines of Go code! How about a full time-series database implementation, robust enough to be run in production for a year where it stored 2.1 trillion data points, and supporting 119M … Continue reading BTrDB: Optimizing Storage System Design for Timeseries Processing
Gorilla: A fast, scalable, in-memory time series database
Gorilla: A fast, scalable, in-memory time series database - Pelkonen et al. 2015 Error rates across one of Facebook's sites were spiking. The problem had first shown up through an automated alert triggered by an in-memory time-series database called Gorilla a few minutes after the problem started. One set of engineers mitigated the immediate issue. … Continue reading Gorilla: A fast, scalable, in-memory time series database
Inferring Causal Impact Using Bayesian Structural Time-Series Models
Inferring Causal Impact Using Bayesian Structural Time-Series Models - Brodersen et al. (Google) 2015 Today's paper comes from 'The Annals of Applied Statistics' - not one of my usual sources (!), and a good indication that I'm likely to be well out of my depth again for parts of it. Nevertheless, it addresses a really … Continue reading Inferring Causal Impact Using Bayesian Structural Time-Series Models
the morning paper 