A review on distance based time series classification
Time series classification is an increasing research topic due to the vast amount of time
series data that is being created over a wide variety of fields. The particularity of the data …
series data that is being created over a wide variety of fields. The particularity of the data …
NI-MWMOTE: An improving noise-immunity majority weighted minority oversampling technique for imbalanced classification problems
J Wei, H Huang, L Yao, Y Hu, Q Fan… - Expert Systems with …, 2020 - Elsevier
Oversampling techniques have been favored by researchers because of their simplicity and
versatility in dealing with imbalanced classification problems. For oversampling techniques …
versatility in dealing with imbalanced classification problems. For oversampling techniques …
Time series cluster kernel for learning similarities between multivariate time series with missing data
Similarity-based approaches represent a promising direction for time series analysis.
However, many such methods rely on parameter tuning, and some have shortcomings if the …
However, many such methods rely on parameter tuning, and some have shortcomings if the …
Early classification of time series by simultaneously optimizing the accuracy and earliness
The problem of early classification of time series appears naturally in contexts where the
data, of temporal nature, are collected over time, and early class predictions are interesting …
data, of temporal nature, are collected over time, and early class predictions are interesting …
A novel crop classification method based on ppfSVM classifier with time-series alignment kernel from dual-polarization SAR datasets
Rapid and accurate crop type mapping is of great significance for agricultural management
and sustainable development. Time-series multi-polarization synthetic aperture radar (SAR) …
and sustainable development. Time-series multi-polarization synthetic aperture radar (SAR) …
Debunking four long-standing misconceptions of time-series distance measures
Distance measures are core building blocks in time-series analysis and the subject of active
research for decades. Unfortunately, the most detailed experimental study in this area is …
research for decades. Unfortunately, the most detailed experimental study in this area is …
A deep machine learning method for classifying cyclic time series of biological signals using time-growing neural network
A Gharehbaghi, M Lindén - IEEE transactions on neural …, 2017 - ieeexplore.ieee.org
This paper presents a novel method for learning the cyclic contents of stochastic time series:
the deep time-growing neural network (DTGNN). The DTGNN combines supervised and …
the deep time-growing neural network (DTGNN). The DTGNN combines supervised and …
A learning framework for size and type independent transient stability prediction of power system using twin convolutional support vector machine
AB Mosavi, A Amiri, H Hosseini - IEEE Access, 2018 - ieeexplore.ieee.org
Real-time transient stability assessment (TSA) of power systems is an important real world
problem in electrical energy engineering and pattern recognition scope. The definition of …
problem in electrical energy engineering and pattern recognition scope. The definition of …
[PDF][PDF] Distance Measures for Time Series in R: The TSdist Package.
The definition of a distance measure between time series is critical for many time series data
mining tasks such as clustering and classification. For this reason, and based on the specific …
mining tasks such as clustering and classification. For this reason, and based on the specific …
Multivariate time-series classification using the hidden-unit logistic model
We present a new model for multivariate time-series classification, called the hidden-unit
logistic model (HULM), that uses binary stochastic hidden units to model latent structure in …
logistic model (HULM), that uses binary stochastic hidden units to model latent structure in …