This site contains data, reference results and links to code for Time Series Classification (TSC), Time Series Clustering (TSCL) and Time Series Extrinsic ...

The overall goal is to identify a time series as coming from one of possibly many sources or predefined groups, using labeled training data.

Time series classification uses supervised machine learning to analyze multiple labeled classes of time series data and then predict or classify the class ...

Each TS is constructed from one of the UEA & UCR time series classification datasets. We group TS by label and concatenate them to create segments with ...

Discontinued! There is now a larger archive here. We suggest you begin by reading the briefing document in PDF or PowerPoint, which also contains the ...

Explore and run machine learning code with Kaggle Notebooks | Using data from Human Activity Recognition with Smartphones.

The Time Series Classification (TSC) task involves training a model from a collection of time series (real valued, ordered, data) in order to predict a target ...

Dataset listing. The univariate and multivariate classification problems are available in three formats: Weka ARFF, simple text files and aeon ts format.

10 нояб. 2023 г. · The dataset contains 3601 training instances and another 1320 testing instances. Each timeseries corresponds to a measurement of engine noise ...

The datasets are organized based on: Dimensions (univariate or multivarite). Length (<300, >=300, >700). Classes (<10, >=10, >=30).