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Found 6 papers, 6 papers with code
Look Into the LITE in Deep Learning for Time Series Classification
In this paper, we present a new architecture for TSC, the Light Inception with boosTing tEchnique (LITE) with only 2. 34% of the number of parameters of the state-of-the-art InceptionTime model, while preserving performance.
aeon: a Python toolkit for learning from time series
aeon is a unified Python 3 library for all machine learning tasks involving time series.
Establishing a Unified Evaluation Framework for Human Motion Generation: A Comparative Analysis of Metrics
The development of generative artificial intelligence for human motion generation has expanded rapidly, necessitating a unified evaluation framework.
Finding Foundation Models for Time Series Classification with a PreText Task
Over the past decade, Time Series Classification (TSC) has gained an increasing attention.
ShapeDBA: Generating Effective Time Series Prototypes using ShapeDTW Barycenter Averaging
Our approach uses a new form of time series average, the ShapeDTW Barycentric Average.
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.
