SOTAVerified

Time Series Prediction

The goal of Time Series Prediction is to infer the future values of a time series from the past.

Source: Orthogonal Echo State Networks and stochastic evaluations of likelihoods

Papers

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Showing 51–60 of 477 papers

TitleStatusHype
A Review of Graph Neural Networks and Their Applications in Power SystemsCode1
Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic ForecastingCode1
MultiWave: Multiresolution Deep Architectures through Wavelet Decomposition for Multivariate Time Series PredictionCode1
AutoLRS: Automatic Learning-Rate Schedule by Bayesian Optimization on the FlyCode1
Feature Programming for Multivariate Time Series PredictionCode1
PowerMamba: A Deep State Space Model and Comprehensive Benchmark for Time Series Prediction in Electric Power SystemsCode1
CMamba: Channel Correlation Enhanced State Space Models for Multivariate Time Series ForecastingCode1
Evaluation of deep learning models for multi-step ahead time series predictionCode1
Adaptive Graph Convolutional Recurrent Network for Traffic ForecastingCode1
From Fourier to Koopman: Spectral Methods for Long-term Time Series PredictionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CMU-DEMAverage mean absolute error9.06—Unverified
#ModelMetricClaimedVerifiedStatus
1LSTMRMSE0—Unverified