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
Adversarial Examples in Deep Learning for Multivariate Time Series Regression
dependable-cps/adversarial-MTSR
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Due to the tremendous success of deep learning (DL) algorithms in various domains including image recognition and computer vision, researchers started adopting these techniques for solving MTS data mining problems, many of which are targeted for safety-critical and cost-critical applications.
Adjusting for Autocorrelated Errors in Neural Networks for Time Series
Daikon-Sun/AdjustAutocorrelation
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NeurIPS 2021
A common assumption in training neural networks via maximum likelihood estimation on time series is that the errors across time steps are uncorrelated.
Fast and Robust Online Inference with Stochastic Gradient Descent via Random Scaling
We develop a new method of online inference for a vector of parameters estimated by the Polyak-Ruppert averaging procedure of stochastic gradient descent (SGD) algorithms.
Recurrent Trend Predictive Neural Network for Multi-Sensor Fire Detection
We propose a Recurrent Trend Predictive Neural Network (rTPNN) for multi-sensor fire detection based on the trend as well as level prediction and fusion of sensor readings.
Evolving-Graph Gaussian Processes
Graph Gaussian Processes (GGPs) provide a data-efficient solution on graph structured domains.
AutoML Meets Time Series Regression Design and Analysis of the AutoSeries Challenge
Driven by business scenarios, we organized the first Automated Time Series Regression challenge (AutoSeries) for the WSDM Cup 2020.
Factor-augmented tree ensembles
This manuscript proposes to extend the information set of time-series regression trees with latent stationary factors extracted via state-space methods.
Graph Neural Networks for Multivariate Time Series Regression with Application to Seismic Data
stefanbloemheuvel/gcntimeseriesregression
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However, these methods have not been adapted for time series tasks to a great extent.
Knowledge Informed Machine Learning using a Weibull-based Loss Function
tvhahn/weibull-knowledge-informed-ml
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Machine learning can be enhanced through the integration of external knowledge.
Domain Adversarial Spatial-Temporal Network: A Transferable Framework for Short-term Traffic Forecasting across Cities
yihongt/dastnet
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To the best of our knowledge, we are the first to employ adversarial multi-domain adaptation for network-wide traffic forecasting problems.
