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Found 15 papers, 11 papers with code
Series2Vec: Similarity-based Self-supervised Representation Learning for Time Series Classification
Our evaluation of Series2Vec on nine large real-world datasets, along with the UCR/UEA archive, shows enhanced performance compared to current state-of-the-art self-supervised techniques for time series.
Improving Position Encoding of Transformers for Multivariate Time Series Classification
We then proposed a new absolute position encoding method dedicated to time series data called time Absolute Position Encoding (tAPE).
Ranked #1 on
Time Series Classification
on Heartbeat
An Approach to Multiple Comparison Benchmark Evaluations that is Stable Under Manipulation of the Comparate Set
The measurement of progress using benchmarks evaluations is ubiquitous in computer science and machine learning.
Proximity Forest 2.0: A new effective and scalable similarity-based classifier for time series
Time series classification (TSC) is a challenging task due to the diversity of types of feature that may be relevant for different classification tasks, including trends, variance, frequency, magnitude, and various patterns.
Deep Learning for Time Series Classification and Extrinsic Regression: A Current Survey
Time Series Classification and Extrinsic Regression are important and challenging machine learning tasks.
Parameterizing the cost function of Dynamic Time Warping with application to time series classification
The cost of an alignment of two points is a function of the difference in the values of those points.
FRANS: Automatic Feature Extraction for Time Series Forecasting
Traditionally, features used in TSF are handcrafted, which requires domain knowledge and significant data-engineering work.
Disjoint-CNN for Multivariate Time Series Classification
Time series classification algorithms have been mainly dominated by non-deep learning models.
Ranked #1 on
Time Series Classification
on pendigits
Classification of multivariate weakly-labelled time-series with attention
This research identifies a gap in weakly-labelled multivariate time-series classification (TSC), where state-of-the-art TSC models do not per-form well.
MultiRocket: Multiple pooling operators and transformations for fast and effective time series classification
We propose MultiRocket, a fast time series classification (TSC) algorithm that achieves state-of-the-art performance with a tiny fraction of the time and without the complex ensembling structure of many state-of-the-art methods.
Time Series Extrinsic Regression
This paper studies Time Series Extrinsic Regression (TSER): a regression task of which the aim is to learn the relationship between a time series and a continuous scalar variable; a task closely related to time series classification (TSC), which aims to learn the relationship between a time series and a categorical class label.
Monash University, UEA, UCR Time Series Extrinsic Regression Archive
We refer to this problem as Time Series Extrinsic Regression (TSER), where we are interested in a more general methodology of predicting a single continuous value, from univariate or multivariate time series.
Detecting Driver's Distraction using Long-term Recurrent Convolutional Network
In this study we demonstrate a novel Brain Computer Interface (BCI) approach to detect driver distraction events to improve road safety.
Time series classification for varying length series
Research into time series classification has tended to focus on the case of series of uniform length.
Elastic bands across the path: A new framework and methods to lower bound DTW
One of the key time series classification algorithms, the nearest neighbor algorithm with DTW distance (NN-DTW) is very expensive to compute, due to the quadratic complexity of DTW.
