26 datasets found

The dataset used in the paper SigFormer: Signature Transformers for Deep Hedging

Electricity, coal, gas, and fuel prices dataset for testing deep hedging strategies

Commodity prices dataset for testing state-of-the-art generative methods

A collection of multivariate time series with missing observations.

The dataset used in the paper is a time series dataset with autoregressive case where X i = Yi−1 or X i = (Yi−1,..., Yi−p).

The dataset is generated as random-walks on a graph with three nodes. At each time step, a signal is emitted as a random vector sampled from a 15-dimensional multivariate normal...

A multivariate time-series dataset that consists of hourly occupancy rates of lanes in San Francisco.

A multivariate time-series dataset that consists of temporal features and spatial features.

The UEA archive is a collection of multivariate time series datasets where classification is of importance.

The UCR archive is a collection of univariate time series datasets where classification is of importance.

The collection of the daily exchange rates of eight foreign countries including Australia, British, Canada, Switzerland, China, Japan, New Zealand and Singapore ranging from...

The electricity consumption in kWh was recorded every 15 minutes from 2012 to 2014, for n = 321 clients.

The solar power production records in the year of 2006, which is sampled every 10 minutes from 137 PV plants in Alabama State.

Multivariate time series forecasting is an important machine learning problem across many domains, including predictions of solar plant energy output, electricity consumption,...

A synthetic univariate time series dataset created using a unique synthetic time series generator algorithm

The dataset used in the paper is a mixture of gaussians.

The dataset used in the paper is a mixture of gaussians, MNIST digits, and UCI HAR dataset.

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.

Long Short Term Memory Networks for Anomaly Detection in Time Series

Time series anomaly detection method based on VAE with re-Encoder and latent constraint network