Benchmarks
Datasets
Greatest papers with code
sktime: A Unified Interface for Machine Learning with Time Series
We present sktime -- a new scikit-learn compatible Python library with a unified interface for machine learning with time series.
TIME SERIES TIME SERIES ANALYSIS TIME SERIES CLASSIFICATION TIME SERIES FORECASTING
Deep learning for time series classification
• hfawaz/dl-4-tsc
• 
In this context, deep learning has emerged in recent years as one of the most effective methods for tackling the supervised classification task, particularly in the field of computer vision.
ACTIVITY RECOGNITION DATA AUGMENTATION TIME SERIES TIME SERIES ANALYSIS TIME SERIES CLASSIFICATION TRANSFER LEARNING
catch22: CAnonical Time-series CHaracteristics
Capturing the dynamical properties of time series concisely as interpretable feature vectors can enable efficient clustering and classification for time-series applications across science and industry.
DIMENSIONALITY REDUCTION TIME SERIES TIME SERIES ANALYSIS TIME SERIES CLASSIFICATION
hctsa: A Computational Framework for Automated Time-Series Phenotyping Using Massive Feature Extraction
Phenotype measurements frequently take the form of time series, but we currently lack a systematic method for relating these complex data streams to scientifically meaningful outcomes, such as relating the movement dynamics of organisms to their genotype or measurements of brain dynamics of a patient to their disease diagnosis.
Highly comparative time-series analysis: The empirical structure of time series and their methods
This new approach to comparing across diverse scientific data and methods allows us to organize time-series datasets automatically according to their properties, retrieve alternatives to particular analysis methods developed in other scientific disciplines, and automate the selection of useful methods for time-series classification and regression tasks.
Chickenpox Cases in Hungary: a Benchmark Dataset for Spatiotemporal Signal Processing with Graph Neural Networks
• benedekrozemberczki/pytorch_geometric_temporal
• 
Recurrent graph convolutional neural networks are highly effective machine learning techniques for spatiotemporal signal processing.
Deep Neural Network Ensembles for Time Series Classification
Deep neural networks have revolutionized many fields such as computer vision and natural language processing.
Recurrent Neural Networks for Multivariate Time Series with Missing Values
• Han-JD/GRU-D
•
Multivariate time series data in practical applications, such as health care, geoscience, and biology, are characterized by a variety of missing values.
Ranked #4 on
Multivariate Time Series Forecasting
on MuJoCo
MULTIVARIATE TIME SERIES FORECASTING MULTIVARIATE TIME SERIES IMPUTATION TIME SERIES TIME SERIES ANALYSIS TIME SERIES CLASSIFICATION TIME SERIES PREDICTION
Multilevel Wavelet Decomposition Network for Interpretable Time Series Analysis
• AHoke/Multilevel_Wavelet_Decomposition_Network_Pytorch
•
In light of this, in this paper we propose a wavelet-based neural network structure called multilevel Wavelet Decomposition Network (mWDN) for building frequency-aware deep learning models for time series analysis.
GRATIS: GeneRAting TIme Series with diverse and controllable characteristics
The explosion of time series data in recent years has brought a flourish of new time series analysis methods, for forecasting, clustering, classification and other tasks.