About: Monash University, UEA, UCR Time Series Regression Archive

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An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Monash University, UEA, UCR Time Series Regression Archive
Creator	Webb, Geoffrey Petitjean, François Bergmeir, Christoph Tan, Chang
Source	ArXiv
abstract	Time series research has gathered lots of interests in the last decade, especially for Time Series Classification (TSC) and Time Series Forecasting (TSF). Research in TSC has greatly benefited from the University of California Riverside and University of East Anglia (UCR/UEA) Time Series Archives. On the other hand, the advancement in Time Series Forecasting relies on time series forecasting competitions such as the Makridakis competitions, NN3 and NN5 Neural Network competitions, and a few Kaggle competitions. Each year, thousands of papers proposing new algorithms for TSC and TSF have utilized these benchmarking archives. These algorithms are designed for these specific problems, but may not be useful for tasks such as predicting the heart rate of a person using photoplethysmogram (PPG) and accelerometer data. We refer to this problem as Time Series Regression (TSR), where we are interested in a more general methodology of predicting a single continuous value, from univariate or multivariate time series. This prediction can be from the same time series or not directly related to the predictor time series and does not necessarily need to be a future value or depend heavily on recent values. To the best of our knowledge, research into TSR has received much less attention in the time series research community and there are no models developed for general time series regression problems. Most models are developed for a specific problem. Therefore, we aim to motivate and support the research into TSR by introducing the first TSR benchmarking archive. This archive contains 19 datasets from different domains, with varying number of dimensions, unequal length dimensions, and missing values. In this paper, we introduce the datasets in this archive and did an initial benchmark on existing models.
has issue date	2020-06-19(xsd:dateTime)
has license	arxiv
sha1sum (hex)	8e711d80805a18d75965aba1d423f995f2121dac
resource representing a document's title	Monash University, UEA, UCR Time Series Regression Archive
resource representing a document's body	covid:8e711d80805a18d75965aba1d423f995f2121dac#body_text
is schema:about of	named entity 'Regression' named entity 'UCI' named entity 'stratified sampling' named entity 'Italian' named entity 'time series' named entity 'time series' named entity 'Nitrogen Dioxide' named entity 'time series' named entity 'Italian' named entity 'TSF' named entity 'ARIMA' named entity 'LFMC' named entity 'time series' named entity 'training set' named entity 'regression models' named entity 'time series' named entity 'DEM' named entity 'Radiometer' named entity 'time series' named entity 'Nitrogen Oxides' named entity 'TSF' named entity 'time series' named entity 'Monash University' named entity 'ECG' named entity 'air pollution' named entity '3, 4' named entity 'Time Series' named entity 'TSR' named entity 'heart rate' named entity 'Time series forecasting' named entity 'convolutional neural network' named entity 'respiratory rate' named entity 'time series' named entity 'm-bus' named entity 'oxygen saturation' named entity 'time series' named entity 'Exponential Smoothing' named entity 'Apple Watch' named entity 'training set' named entity 'ECG' named entity 'respiratory rate' named entity 'Australian Bureau of Meteorology' named entity 'fluid dynamics' named entity 'time series' named entity 'photoplethysmogram' named entity 'time series' named entity 'transpiration' named entity 'heart rate' named entity 'air quality' named entity 'Apple Watch' named entity '19, 20' named entity 'time series' named entity 'benzene' named entity 'TSR' named entity 'death rate' named entity 'Sentiment analysis' named entity 'time series' named entity 'Machine Learning' named entity 'Kaggle' named entity 'accelerometer' named entity 'accelerometer' named entity 'heart rate' named entity 'UCI' named entity 'LFMC' named entity 'Sentiment Analysis' named entity 'United States' named entity 'Java' named entity 'DEM' named entity 'time series' named entity 'time series' named entity 'ECG' named entity 'photoplethysmogram' named entity 'TSR' named entity 'ECG' named entity 'sliding window' named entity 'June 2020' named entity 'Covid-19 pandemic' named entity 'Beijing' named entity 'air pollutant concentrations' named entity 'weather station' named entity 'active and reactive power' named entity 'relative humidity' named entity 'time series' named entity 'heart rate'

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