About: Time Series Regression

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

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Time Series Regression
Creator	Webb, Geoffrey Petitjean, François Bergmeir, Christoph Tan, Chang
source	ArXiv
abstract	This paper introduces Time Series Regression (TSR): a little-studied task of which the aim is to learn the relationship between a time series and a continuous target variable. In contrast to time series classification (TSC), which predicts a categorical class label, TSR predicts a numerical value. This task generalizes forecasting, relaxing the requirement that the value predicted be a future value of the input series or primarily depend on more recent values. In this paper, we motivate and introduce this task, and benchmark possible solutions to tackling it on a novel archive of 19 TSR datasets which we have assembled. Our results show that the state-of-the-art TSC model Rocket, when adapted for regression, performs the best overall compared to other TSC models and state-of-the-art machine learning (ML) models such as XGBoost, Random Forest and Support Vector Regression.More importantly, we show that much research is needed in this field to improve the accuracy of ML models.
has issue date	2020-06-23(xsd:dateTime)
has license	arxiv
sha1sum (hex)	e24d82d4595bf22a6f5267d83fc409cd6ceaf42f
resource representing a document's title	Time Series Regression
resource representing a document's body	covid:e24d82d4595bf22a6f5267d83fc409cd6ceaf42f#body_text
is schema:about of	named entity 'AIM' named entity 'VALUES' named entity 'TIME SERIES' named entity 'TIME SERIES CLASSIFICATION' named entity 'PAPER' named entity 'TARGET' named entity 'CATEGORICAL' named entity 'CLASS' named entity 'time series' named entity 'Weighted average' named entity 'k-NN' named entity 'time series' named entity 'feature extraction' named entity 'feature vector' named entity 'root mean squared error' named entity 'Convolutional Neural Networks' named entity 'RMSE' named entity 'regression models' named entity 'regression models' named entity 'regression techniques' named entity 'machine learning' named entity 'TSR' named entity 'TSR' named entity 'heart rate' named entity 'time series' named entity 'BIDMC' named entity 'TSF' named entity 'photoplethysmogram' named entity 'sentiment analysis' named entity 'XGBoost' named entity 'regression problem' named entity 'signal processing' named entity 'moisture content' named entity 'time series data' named entity 'climate change' named entity 'time series' named entity 'sampling' named entity 'time series' named entity 'hidden layers' named entity 'TSR' named entity 'decision trees' named entity 'univariate' named entity 'sensor' named entity 'feature vector' named entity 'ECG' named entity 'Exponential Smoothing' named entity 'softmax' named entity '200%' named entity 'respiratory rate' named entity 'time series' named entity 'polynomial' named entity 'target variable' named entity 'Java' named entity 'temporal dimension' named entity 'unnormalised' named entity 'batch normalization' named entity 'feature vector' named entity 'Regression problem' named entity 'higher-dimensional space' named entity 'variance' named entity 'ResNet' named entity 'XGBoost' named entity 'open source' named entity 'Random Forest' named entity 'time series' named entity 'SVR' named entity 'linearly separable' named entity 'time series data' named entity 'target variable' named entity 'Gradient Boosting' named entity 'forecasting models' named entity 'heart rate' named entity 'LFMC' named entity 'social media' named entity 'social media' named entity 'Time Series' named entity 'univariate time series' named entity 'change over time' named entity 'Euclidean distance' named entity 'time series data' named entity 'time series data' named entity 'database' named entity 'frequency domain' named entity 'time series analysis'

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