Related references
Note: Only part of the references are listed.The great multivariate time series classification bake off: a review and experimental evaluation of recent algorithmic advances
Alejandro Pasos Ruiz et al.
DATA MINING AND KNOWLEDGE DISCOVERY (2021)
Tibial Acceleration-Based Prediction of Maximal Vertical Loading Rate During Overground Running: A Machine Learning Approach
Rud Derie et al.
FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY (2020)
A general anomaly detection framework for fleet-based condition monitoring of machines
Kilian Hendrickx et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2020)
The autofeat Python Library for Automated Feature Engineering and Selection
Franziska Horn et al.
MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES, ECML PKDD 2019, PT I (2020)
A scalable robust and automatic propositionalization approach for Bayesian classification of large mixed numerical and categorical data
Marc Boulle et al.
MACHINE LEARNING (2019)
Deep learning for time series classification: a review
Hassan Ismail Fawaz et al.
DATA MINING AND KNOWLEDGE DISCOVERY (2019)
Using Machine Learning to Discern Eruption in Noisy Environments: A Case Study Using CO2-Driven Cold-Water Geyser in Chimayo, New Mexico
Baichuan Yuan et al.
SEISMOLOGICAL RESEARCH LETTERS (2019)
Chemiresistive Sensor Array and Machine Learning Classification of Food
Vera Schroeder et al.
ACS SENSORS (2019)
catch22: CAnonical Time-series CHaracteristics Selected through highly comparative time-series analysis
Carl H. Lubba et al.
DATA MINING AND KNOWLEDGE DISCOVERY (2019)
Time Series FeatuRe Extraction on basis of Scalable Hypothesis tests (tsfresh - A Python package)
Maximilian Christ et al.
NEUROCOMPUTING (2018)
Data fusion of body-worn accelerometers and heart rate to predict VO2max during submaximal running
Arne De Brabandere et al.
PLOS ONE (2018)
Activity Recognition Invariant to Sensor Orientation with Wearable Motion Sensors
Aras Yurtman et al.
SENSORS (2017)
hctsa: A Computational Framework for Automated Time-Series Phenotyping Using Massive Feature Extraction
Ben D. Fulcher et al.
CELL SYSTEMS (2017)
Automated Feature Design for Numeric Sequence Classification by Genetic Programming
Dustin Y. Harvey et al.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION (2015)
Window-Based Feature Engineering for Prediction of Methane Threats in Coal Mines
Marek Grzegorowski et al.
ROUGH SETS, FUZZY SETS, DATA MINING, AND GRANULAR COMPUTING, RSFDGRC 2015 (2015)
A survey on feature selection methods
Girish Chandrashekar et al.
COMPUTERS & ELECTRICAL ENGINEERING (2014)
CID: an efficient complexity-invariant distance for time series
Gustavo E. A. P. A. Batista et al.
DATA MINING AND KNOWLEDGE DISCOVERY (2014)
A Feature Fusion Based Forecasting Model for Financial Time Series
Zhiqiang Guo et al.
PLOS ONE (2014)
A Review and Taxonomy of Activity Recognition on Mobile Phones
Ozlem Durmaz Incel et al.
BIONANOSCIENCE (2013)
uWave: Accelerometer-based personalized gesture recognition and its applications
Jiayang Liu et al.
PERVASIVE AND MOBILE COMPUTING (2009)
PREDICTIVE LEARNING VIA RULE ENSEMBLES
Jerome H. Frieman et al.
ANNALS OF APPLIED STATISTICS (2008)
Exact indexing of dynamic time warping
E Keogh et al.
KNOWLEDGE AND INFORMATION SYSTEMS (2005)
Harnessing nonlinearity: Predicting chaotic systems and saving energy in wireless communication
H Jaeger et al.
SCIENCE (2004)
Financial time series forecasting using support vector machines
KJ Kim
NEUROCOMPUTING (2003)
Ambulatory estimates of maximal aerobic power from foot-ground contact times and heart rates in running humans
PG Weyand et al.
JOURNAL OF APPLIED PHYSIOLOGY (2001)
Extracting model equations from experimental data
R Friedrich et al.
PHYSICS LETTERS A (2000)
PhysioBank, PhysioToolkit, and PhysioNet - Components of a new research resource for complex physiologic signals
AL Goldberger et al.
CIRCULATION (2000)