16 datasets found

The dataset is a vector autoregressive (VAR) model with trend, where the mean is modeled by a recurrent neural network of the LSTM type, and the dependence structure is modeled...

The Mackey-Glass dataset is used as an example in the paper.

The Lorenz system is a well-known nonlinear dynamical system to model convective hydrodynamic flows. The consequence of chaos is that, under certain parameterizations, the...

Time series data can be represented in both the time and frequency domains, with the time domain emphasizing local dependencies and the frequency domain highlighting global...

The dataset consists of real-life solar power production data.

The dataset used in the paper is a synthetic dataset consisting of points in 2D that follow a spiral distribution.

The dataset consists of time series data in the form of spring oscillations, solar power production data, and spiral data.

Global Sea Level Pressure (SLP) dataset provided by NCEP/National Center for Atmospheric Research (NCAR) Reanalysis Project

The dataset used in the paper is COVID-19 case data, state restriction policy, population and density, population with higher risk, age structure data, race structure data, and...

The dataset used in the paper is a collection of temperature sequences from beehives, including external, peripheral, and core temperatures.

UCR Time Series Archive is a collection of time series datasets from various domains. It is used for training and testing deep neural networks for time series classification tasks.

The Santa Fe Laser dataset consists in a time-series of normalized sampled intensities from an infra-red laser in chaotic regime. The Sunspot dataset is a time-series of monthly...

The Santa Fe Laser dataset consists in a time-series of normalized sampled intensities from an infra-red laser in chaotic regime. The Sunspot dataset is a time-series of monthly...

The dataset used in this paper is a time series data, which is used to test the performance of the Conditional Generative Adversarial Net (CGAN) method.

Time series data for classification tasks using data in http://www.timeseriesclassification.com/

A regression problem where the true coefficient function β is sparse and smooth, in order to show how the adaptive centering is able to jointly shrink towards a mixed target.