time-series-5

Time Series Regression

3260 papers • 126 benchmarks • 313 datasets

Predicting one or more scalars for an entire time series example.

(Image credit: Papersgraph)

Benchmarks

These leaderboards are used to track progress in time-series-regression-10

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Dataset

Best Model

Actions

USNA-Cn2 (short-duration)

USNA-Cn2 (long-term)

Libraries

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Use these libraries to find time-series-regression-10 models and implementations

Datasets

Appliances Energy

Energy Consumption Curves of 499 Customers from Spain

MLO-Cn2

USNA-Cn2 (long-term)

USNA-Cn2 (short-duration)

Subtasks

COVID-19 Modelling

Most implemented papers

A Transformer-based Framework for Multivariate Time Series Representation Learning

George Zerveas, Srideepika Jayaraman, Dhaval Patel, A. Bhamidipaty, Carsten Eickhoff•Mon Oct 05 2020

A novel framework for multivariate time series representation learning based on the transformer encoder architecture, which can offer substantial performance benefits over fully supervised learning on downstream tasks, both with but even without leveraging additional unlabeled data, i.e., by reusing the existing data samples.

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MLO-Cn2

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A Multi-Horizon Quantile Recurrent Forecaster

Ruofeng Wen, K. Torkkola, Balakrishnan Narayanaswamy, Dhruv Madeka•Tue Nov 28 2017

A framework for general probabilistic multi-step time series regression that exploits the expressiveness and temporal nature of Recurrent Neural Networks, the nonparametric nature of Quantile Regression and the efficiency of Direct Multi-Horizon Forecasting is proposed.

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Monash University, UEA, UCR Time Series Extrinsic Regression Archive

Geoffrey I. Webb, F. Petitjean, Chang Wei Tan, C. Bergmeir•Thu Jun 18 2020

This paper introduces the first TSER benchmarking archive, which contains 19 datasets from different domains, with varying number of dimensions, unequal length dimensions, and missing values, and did an initial benchmark on existing models.

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Changepoint Detection in Noisy Data Using a Novel Residuals Permutation-Based Method (RESPERM): Benchmarking and Application to Single Trial ERPs

K. Kotowski, Piotr Fabian, K. Stapor, W. Sommer, G. Kończak, Jeremi K. Ochab, A. Bereś, G. Ślusarczyk•Wed Apr 20 2022

The optimal changepoint in RESPERM maximizes Cohen’s effect size with the parameters estimated by the permutation of residuals in a linear model, making it the method of choice in neuroscience, medicine and many other fields.

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Multiobjective Programming for Type-2 Hierarchical Fuzzy Inference Trees

Varun Ojha, A. Abraham, V. Snás̃el•Mon May 15 2017

The proposed multiobjective HFIT provided less complex and highly accurate models compared to the models produced by the most of other methods, and is an efficient and competitive alternative to the other FISs for function approximation and feature selection.

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Ensemble of heterogeneous flexible neural trees using multiobjective genetic programming

Varun Ojha, A. Abraham, V. Snás̃el•Tue Feb 28 2017

The heterogeneous creation of HFNT proved to be efficient in making ensemble system from the final population, and a comprehensive test over classification, regression, and time-series datasets proved the efficiency of the proposed algorithm over other available prediction methods.

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Deconvolutional Time Series Regression: A Technique for Modeling Temporally Diffuse Effects

Cory Shain, William Schuler•Sun Dec 31 2017

A new statistical model is proposed that borrows from digital signal processing by recasting the predictors and response as convolutionally-related signals, using recent advances in machine learning to fit latent impulse response functions (IRFs) of arbitrary shape.

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High-Dimensional Granger Causality Tests with an Application to VIX and News

Andrii Babii, Eric Ghysels, Jonas Striaukas•Thu May 28 2020

The debiased central limit theorem for low-dimensional groups of regression coefficients is established and the HAC estimator of the long-run variance based on the sg-LASSO residuals is studied, which leads to valid time-series inference for individual regression coefficients as well as groups, including Granger causality tests.

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Discretize-Optimize vs. Optimize-Discretize for Time-Series Regression and Continuous Normalizing Flows

Lars Ruthotto, Derek Onken•Tue May 26 2020

Disc-Opt methods can achieve similar performance as Opt-Disc at inference with drastically reduced training costs using neural ODEs for time-series regression and continuous normalizing flows (CNFs).

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Time series extrinsic regression

Geoffrey I. Webb, F. Petitjean, Chang Wei Tan, C. Bergmeir•Mon Jun 22 2020

The results show that the state-of-the-art TSC algorithm Rocket, when adapted for regression, achieves the highest overall accuracy compared to adaptations of other TSC algorithms and state of theart machine learning (ML) algorithms such as XGBoost, Random Forest and Support Vector Regression.

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