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 126–150 of 477 papers

TitleStatusHype
Learn to Predict Vertical Track Irregularity with Extremely Imbalanced Data—0
A novel Reservoir Architecture for Periodic Time Series Prediction—0
Active Tuning—0
Cellular Traffic Prediction and Classification: a comparative evaluation of LSTM and ARIMA—0
Explainable Multi-modal Time Series Prediction with LLM-in-the-Loop—0
Composite FORCE learning of chaotic echo state networks for time-series prediction—0
Comparison of Traditional and Hybrid Time Series Models for Forecasting COVID-19 Cases—0
A novel hybrid model based on multi-objective Harris hawks optimization algorithm for daily PM2.5 and PM10 forecasting—0
Comparison between DeepESNs and gated RNNs on multivariate time-series prediction—0
Comparing seven methods for state-of-health time series prediction for the lithium-ion battery packs of forklifts—0
A federated large language model for long-term time series forecasting—0
A fast noise filtering algorithm for time series prediction using recurrent neural networks—0
An Optimized and Energy-Efficient Parallel Implementation of Non-Iteratively Trained Recurrent Neural Networks—0
Event prediction and causality inference despite incomplete information—0
Evolutionary Ensemble Learning for Multivariate Time Series Prediction—0
Excess risk bound for deep learning under weak dependence—0
Cloud Cover Nowcasting with Deep Learning—0
Characterizing Learning in Spiking Neural Networks with Astrocyte-Like Units—0
A Self-organizing Interval Type-2 Fuzzy Neural Network for Multi-Step Time Series Prediction—0
A Family of Robust Generalized Adaptive Filters and Application for Time-series Prediction—0
Chaotic Time Series Prediction using Spatio-Temporal RBF Neural Networks—0
CGS-Mask: Making Time Series Predictions Intuitive for All—0
Can Competition Enhance the Proficiency of Agents Powered by Large Language Models in the Realm of News-driven Time Series Forecasting?—0
Evaluating Preprocessing Strategies for Time Series Prediction Using Deep Learning Architectures—0
Ensemble quantile-based deep learning framework for streamflow and flood prediction in Australian catchments—0
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Benchmark Results

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