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

Showing 150 of 477 papers

TitleStatusHype
GluonTS: Probabilistic Time Series Models in PythonCode3
Mamba Meets Financial Markets: A Graph-Mamba Approach for Stock Price PredictionCode2
Closed-form Continuous-time Neural ModelsCode2
PDFormer: Propagation Delay-Aware Dynamic Long-Range Transformer for Traffic Flow PredictionCode2
Liquid Time-constant NetworksCode2
Trainable Fractional Fourier TransformCode2
Bayesian Temporal Factorization for Multidimensional Time Series PredictionCode2
An Extensive Data Processing Pipeline for MIMIC-IVCode2
UnetTSF: A Better Performance Linear Complexity Time Series Prediction ModelCode2
Deep Learning for Time Series Forecasting: Tutorial and Literature SurveyCode2
MG-TSD: Multi-Granularity Time Series Diffusion Models with Guided Learning ProcessCode2
PRformer: Pyramidal Recurrent Transformer for Multivariate Time Series ForecastingCode2
LibCity: An Open Library for Traffic PredictionCode2
MultiWave: Multiresolution Deep Architectures through Wavelet Decomposition for Multivariate Time Series PredictionCode1
Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic ForecastingCode1
MG-TSD: Multi-Granularity Time Series Diffusion Models with Guided Learning Process Download PDFCode1
Deep Switching Auto-Regressive Factorization:Application to Time Series ForecastingCode1
CVaR-based Flight Energy Risk Assessment for Multirotor UAVs using a Deep Energy ModelCode1
MFRFNN: Multi-Functional Recurrent Fuzzy Neural Network for Chaotic Time Series PredictionCode1
Non-Gaussian Gaussian Processes for Few-Shot RegressionCode1
CMoS: Rethinking Time Series Prediction Through the Lens of Chunk-wise Spatial CorrelationsCode1
An Extreme-Adaptive Time Series Prediction Model Based on Probability-Enhanced LSTM Neural NetworksCode1
Deep and Confident Prediction for Time Series at UberCode1
MemDA: Forecasting Urban Time Series with Memory-based Drift AdaptationCode1
DeepSITH: Efficient Learning via Decomposition of What and When Across Time ScalesCode1
Deep Stock PredictionsCode1
Leveraging 2D Information for Long-term Time Series Forecasting with Vanilla TransformersCode1
Dimensionality reduction to maximize prediction generalization capabilityCode1
Adaptive Graph Convolutional Recurrent Network for Traffic ForecastingCode1
Multi-step-ahead Stock Price Prediction Using Recurrent Fuzzy Neural Network and Variational Mode DecompositionCode1
Learning from Polar Representation: An Extreme-Adaptive Model for Long-Term Time Series ForecastingCode1
An Evaluation of Deep Learning Models for Stock Market Trend PredictionCode1
Clinical Risk Prediction with Temporal Probabilistic Asymmetric Multi-Task LearningCode1
Legendre Memory Units: Continuous-Time Representation in Recurrent Neural NetworksCode1
From Fourier to Koopman: Spectral Methods for Long-term Time Series PredictionCode1
Financial time series forecasting with multi-modality graph neural networkCode1
Generative Network-Based Reduced-Order Model for Prediction, Data Assimilation and Uncertainty QuantificationCode1
Bayesian Neural Architecture Search using A Training-Free Performance MetricCode1
SIGMA: Selective Gated Mamba for Sequential RecommendationCode1
AA-Forecast: Anomaly-Aware Forecast for Extreme EventsCode1
IMTS is Worth Time Channel Patches: Visual Masked Autoencoders for Irregular Multivariate Time Series PredictionCode1
Evaluation of deep learning models for multi-step ahead time series predictionCode1
AutoLRS: Automatic Learning-Rate Schedule by Bayesian Optimization on the FlyCode1
Extended Deep Adaptive Input Normalization for Preprocessing Time Series Data for Neural NetworksCode1
A Novel Deep Learning Model for Hotel Demand and Revenue Prediction amid COVID-19Code1
CMamba: Channel Correlation Enhanced State Space Models for Multivariate Time Series ForecastingCode1
A Review of Graph Neural Networks and Their Applications in Power SystemsCode1
Conformal PID Control for Time Series PredictionCode1
DAG-Net: Double Attentive Graph Neural Network for Trajectory ForecastingCode1
Error-quantified Conformal Inference for Time SeriesCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CMU-DEMAverage mean absolute error9.06Unverified
#ModelMetricClaimedVerifiedStatus
1LSTMRMSE0Unverified