Time Series Regression
29 papers with code • 3 benchmarks • 6 datasets
Predicting one or more scalars for an entire time series example.
Benchmarks
These leaderboards are used to track progress in Time Series Regression
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Datasets
Appliances Energy
MLO-Cn2
Most implemented papers
A Transformer-based Framework for Multivariate Time Series Representation Learning
In this work we propose for the first time a transformer-based framework for unsupervised representation learning of multivariate time series.
A Multi-Horizon Quantile Recurrent Forecaster
We propose a framework for general probabilistic multi-step time series regression.
Monash University, UEA, UCR Time Series Extrinsic Regression Archive
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).
Multiobjective Programming for Type-2 Hierarchical Fuzzy Inference Trees
Hence, the proposed HFIT is an efficient and competitive alternative to the other FISs for function approximation and feature selection.
Ensemble of heterogeneous flexible neural trees using multiobjective genetic programming
Machine learning algorithms are inherently multiobjective in nature, where approximation error minimization and model's complexity simplification are two conflicting objectives.
Deconvolutional Time Series Regression: A Technique for Modeling Temporally Diffuse Effects
Researchers in computational psycholinguistics frequently use linear models to study time series data generated by human subjects.
High-Dimensional Granger Causality Tests with an Application to VIX and News
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
EmoryMLIP/DOvsOD_NeuralODEs
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Neural ODEs are ordinary differential equations (ODEs) with neural network components.
Time Series Extrinsic Regression
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.
