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 21–30 of 477 papers

TitleStatusHype
SIGMA: Selective Gated Mamba for Sequential RecommendationCode1
CMamba: Channel Correlation Enhanced State Space Models for Multivariate Time Series ForecastingCode1
Leveraging 2D Information for Long-term Time Series Forecasting with Vanilla TransformersCode1
Time Series Forecasting with LLMs: Understanding and Enhancing Model CapabilitiesCode1
MG-TSD: Multi-Granularity Time Series Diffusion Models with Guided Learning Process Download PDFCode1
STanHop: Sparse Tandem Hopfield Model for Memory-Enhanced Time Series PredictionCode1
How Does It Function? Characterizing Long-term Trends in Production Serverless WorkloadsCode1
Learning from Polar Representation: An Extreme-Adaptive Model for Long-Term Time Series ForecastingCode1
Extended Deep Adaptive Input Normalization for Preprocessing Time Series Data for Neural NetworksCode1
TACTiS-2: Better, Faster, Simpler Attentional Copulas for Multivariate Time SeriesCode1
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Benchmark Results

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