Time Series Regression
26 papers with code • 0 benchmarks • 2 datasets
Predicting one or more scalars for an entire time series example.
These leaderboards are used to track progress in Time Series Regression
In this work we propose for the first time a transformer-based framework for unsupervised representation learning of multivariate time series.
We refer to this problem as Time Series Extrinsic Regression (TSER), where we are interested in a more general methodology of predicting a single continuous value, from univariate or multivariate time series.
Changepoint Detection in Noisy Data Using a Novel Residuals Permutation-Based Method (RESPERM): Benchmarking and Application to Single Trial ERPs
Here, we present a new method for detecting a single changepoint in a linear time series regression model, termed residuals permutation-based method (RESPERM).
Hence, the proposed HFIT is an efficient and competitive alternative to the other FISs for function approximation and feature selection.
Machine learning algorithms are inherently multiobjective in nature, where approximation error minimization and model's complexity simplification are two conflicting objectives.
Researchers in computational psycholinguistics frequently use linear models to study time series data generated by human subjects.
We establish the debiased central limit theorem for low dimensional groups of regression coefficients and study the HAC estimator of the long-run variance based on the sparse-group LASSO residuals.
Discretize-Optimize vs. Optimize-Discretize for Time-Series Regression and Continuous Normalizing Flows
Neural ODEs are ordinary differential equations (ODEs) with neural network components.
This paper studies Time Series Extrinsic Regression (TSER): a regression task of which the aim is to learn the relationship between a time series and a continuous scalar variable; a task closely related to time series classification (TSC), which aims to learn the relationship between a time series and a categorical class label.