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Found 6 papers, 5 papers with code
tsdownsample: high-performance time series downsampling for scalable visualization
We achieve this optimization by leveraging low-level SIMD instructions and multithreading capabilities in Rust.
Do Not Sleep on Traditional Machine Learning: Simple and Interpretable Techniques Are Competitive to Deep Learning for Sleep Scoring
We show that, for the sleep stage scoring task, the expressiveness of an engineered feature vector is on par with the internally learned representations of deep learning models.
Ranked #1 on
Multimodal Sleep Stage Detection
on Sleep-EDF-SC
Automatic Sleep Stage Classification
BIG-bench Machine Learning
+2
Plotly-Resampler: Effective Visual Analytics for Large Time Series
We observe that open source Python visualization toolkits empower data scientists in most visual analytics tasks, but lack the combination of scalability and interactivity to realize effective time series visualization.
Powershap: A Power-full Shapley Feature Selection Method
Benchmarks and simulations show that powershap outperforms other filter methods with predictive performances on par with wrapper methods while being significantly faster, often even reaching half or a third of the execution time.
Deep Learning for Effective and Efficient Reduction of Large Adaptation Spaces in Self-Adaptive Systems
DLASeR+ offers an extendable learning framework for online adaptation space reduction that does not require feature engineering, while supporting three common types of adaptation goals: threshold, optimization, and set-point goals.
tsflex: flexible time series processing & feature extraction
$\texttt{tsflex}$ is flexible as it supports (1) multivariate time series, (2) multiple window-stride configurations, and (3) integrates with processing and feature functions from other packages, while (4) making no assumptions about the data sampling regularity, series alignment, and data type.
