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 1–25 of 477 papers

TitleStatusHype
GluonTS: Probabilistic Time Series Models in PythonCode3
Mamba Meets Financial Markets: A Graph-Mamba Approach for Stock Price PredictionCode2
PRformer: Pyramidal Recurrent Transformer for Multivariate Time Series ForecastingCode2
MG-TSD: Multi-Granularity Time Series Diffusion Models with Guided Learning ProcessCode2
Trainable Fractional Fourier TransformCode2
UnetTSF: A Better Performance Linear Complexity Time Series Prediction ModelCode2
PDFormer: Propagation Delay-Aware Dynamic Long-Range Transformer for Traffic Flow PredictionCode2
An Extensive Data Processing Pipeline for MIMIC-IVCode2
LibCity: An Open Library for Traffic PredictionCode2
Closed-form Continuous-time Neural ModelsCode2
Liquid Time-constant NetworksCode2
Deep Learning for Time Series Forecasting: Tutorial and Literature SurveyCode2
Bayesian Temporal Factorization for Multidimensional Time Series PredictionCode2
IMTS is Worth Time Channel Patches: Visual Masked Autoencoders for Irregular Multivariate Time Series PredictionCode1
CMoS: Rethinking Time Series Prediction Through the Lens of Chunk-wise Spatial CorrelationsCode1
Error-quantified Conformal Inference for Time SeriesCode1
SWIFT: Mapping Sub-series with Wavelet Decomposition Improves Time Series ForecastingCode1
PowerMamba: A Deep State Space Model and Comprehensive Benchmark for Time Series Prediction in Electric Power SystemsCode1
Recursive Gaussian Process State Space ModelCode1
An Evaluation of Deep Learning Models for Stock Market Trend PredictionCode1
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
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Benchmark Results

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