Matrix profile XI: SCRIMP++: time series motif discovery at interactive speeds
Time series motif discovery is an important primitive for time series analytics, and is used in
domains as diverse as neuroscience, music and sports analytics. In recent years, algorithmic …
domains as diverse as neuroscience, music and sports analytics. In recent years, algorithmic …
ClaSP: parameter-free time series segmentation
The study of natural and human-made processes often results in long sequences of
temporally-ordered values, aka time series (TS). Such processes often consist of multiple …
temporally-ordered values, aka time series (TS). Such processes often consist of multiple …
[PDF][PDF] STUMPY: A powerful and scalable Python library for time series data mining
SM Law - Journal of Open Source Software, 2019 - joss.theoj.org
Direct visualization, summary statistics (ie, minimum, maximum, mean, standard deviation),
ARIMA models, anomaly detection, forecasting, clustering, and deep learning are all popular …
ARIMA models, anomaly detection, forecasting, clustering, and deep learning are all popular …
Identifying candidate routines for robotic process automation from unsegmented UI logs
Robotic Process Automation (RPA) is a technology to develop software bots that automate
repetitive sequences of interactions between users and software applications (aka routines) …
repetitive sequences of interactions between users and software applications (aka routines) …
Espresso: Entropy and shape aware time-series segmentation for processing heterogeneous sensor data
Extracting informative and meaningful temporal segments from high-dimensional wearable
sensor data, smart devices, or IoT data is a vital preprocessing step in applications such as …
sensor data, smart devices, or IoT data is a vital preprocessing step in applications such as …
Time series clustering based on normal cloud model and complex network
H Li, M Chen - Applied Soft Computing, 2023 - Elsevier
When data mining research is conducted, it is difficult to obtain precise domain knowledge to
set a similarity threshold. Furthermore, noise and missing values are inevitable. Missing …
set a similarity threshold. Furthermore, noise and missing values are inevitable. Missing …
Towards Learning Discrete Representations via Self-Supervision for Wearables-Based Human Activity Recognition
Human activity recognition (HAR) in wearable and ubiquitous computing typically involves
translating sensor readings into feature representations, either derived through dedicated …
translating sensor readings into feature representations, either derived through dedicated …
[HTML][HTML] On the use of matrix profiles and optimal transport theory for multivariate time series anomaly detection within structural health monitoring
In order for a practical application of structural health monitoring to be considered
successful, not only is the detection of anomalies important but so is the tracking of various …
successful, not only is the detection of anomalies important but so is the tracking of various …
The Swiss army knife of time series data mining: ten useful things you can do with the matrix profile and ten lines of code
The recently introduced data structure, the Matrix Profile, annotates a time series by
recording the location of and distance to the nearest neighbor of every subsequence. This …
recording the location of and distance to the nearest neighbor of every subsequence. This …
Discovering data transfer routines from user interaction logs
Abstract Robotic Process Automation (RPA) is a technology to automate routine work such
as copying data across applications or filling in document templates using data from multiple …
as copying data across applications or filling in document templates using data from multiple …