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 301–325 of 477 papers

TitleStatusHype
Time-to-Green predictions for fully-actuated signal control systems with supervised learning—0
TLNets: Transformation Learning Networks for long-range time-series prediction—0
Transformer based time series prediction of the maximum power point for solar photovoltaic cells—0
TrTr: A Versatile Pre-Trained Large Traffic Model based on Transformer for Capturing Trajectory Diversity in Vehicle Population—0
TSP-OCS: A Time-Series Prediction for Optimal Camera Selection in Multi-Viewpoint Surgical Video Analysis—0
Two-phase flow regime prediction using LSTM based deep recurrent neural network—0
Unconventional Computing based on Four Wave Mixing in Highly Nonlinear Waveguides—0
Deep incremental learning models for financial temporal tabular datasets with distribution shifts—0
Unsupervised Learning in Reservoir Computing for EEG-based Emotion Recognition—0
Unveiling the role of plasticity rules in reservoir computing—0
U-shaped Transformer: Retain High Frequency Context in Time Series Analysis—0
Using an Ancillary Neural Network to Capture Weekends and Holidays in an Adjoint Neural Network Architecture for Intelligent Building Management—0
Using Connectome Features to Constrain Echo State Networks—0
Water Quality Prediction on a Sigfox-compliant IoT Device: The Road Ahead of WaterS—0
Wavelength-multiplexed Delayed Inputs for Memory Enhancement of Microring-based Reservoir Computing—0
Wavelet-Enhanced Neural ODE and Graph Attention for Interpretable Energy Forecasting—0
When Traffic Flow Prediction Meets Wireless Big Data Analytics—0
Layer-wise Relevance Propagation for Echo State Networks applied to Earth System Variability—0
Joint Forecasting and Interpolation of Graph Signals Using Deep Learning—0
Causal Modeling of Policy Interventions From Sequences of Treatments and Outcomes—0
Kernel Least Mean Square with Adaptive Kernel Size—0
Landslide Surface Displacement Prediction Based on VSXC-LSTM Algorithm—0
Large Language Model (LLM) for Telecommunications: A Comprehensive Survey on Principles, Key Techniques, and Opportunities—0
Large-Scale Spectrum Occupancy Learning via Tensor Decomposition and LSTM Networks—0
Learning Hamiltonian Dynamics with Bayesian Data Assimilation—0
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Benchmark Results

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