SOTAVerified

Time Series Analysis

Time Series Analysis is a statistical technique used to analyze and model time-based data. It is used in various fields such as finance, economics, and engineering to analyze patterns and trends in data over time. The goal of time series analysis is to identify the underlying patterns, trends, and seasonality in the data, and to use this information to make informed predictions about future values.

( Image credit: Autoregressive CNNs for Asynchronous Time Series )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 6351–6400 of 6748 papers

TitleStatusHype
Dynamic survival prediction in intensive care units from heterogeneous time series without the need for variable selection or pre-processing—0
Dynamic Temporal Reconciliation by Reinforcement learning—0
Dynamic Time Warp Convolutional Networks—0
Dynamic Weight Alignment for Temporal Convolutional Neural Networks—0
Dynamic Window-level Granger Causality of Multi-channel Time Series—0
DynImp: Dynamic Imputation for Wearable Sensing Data Through Sensory and Temporal Relatedness—0
E2GAN: End-to-End Generative Adversarial Network or Multivariate Time Series Imputation—0
EA-LSTM: Evolutionary Attention-based LSTM for Time Series Prediction—0
Earliness-Aware Deep Convolutional Networks for Early Time Series Classification—0
Early and Revocable Time Series Classification—0
Early Classification for Agricultural Monitoring from Satellite Time Series—0
Early Classification of Time Series. Cost-based Optimization Criterion and Algorithms—0
Early Classification of Time Series is Meaningful—0
Early Detection of Forest Calamities in Homogeneous Stands -- Deep Learning Applied to Bark-Beetle Outbreaks—0
Early Detection of Thermoacoustic Instabilities in a Cryogenic Rocket Thrust Chamber using Combustion Noise Features and Machine Learning—0
Early Discovery of Disappearing Entities in Microblogs—0
Early Predictions for Medical Crowdfunding: A Deep Learning Approach Using Diverse Inputs—0
Early Recognition of Ball Catching Success in Clinical Trials with RNN-Based Predictive Classification—0
Early Recognition of Human Activities from First-Person Videos Using Onset Representations—0
Early Time-Series Classification Algorithms: An Empirical Comparison—0
SeismoGen: Seismic Waveform Synthesis Using Generative Adversarial Networks—0
EB-dynaRE: Real-Time Adjustor for Brownian Movement with Examples of Predicting Stock Trends Based on a Novel Event-Based Supervised Learning Algorithm—0
eCDANs: Efficient Temporal Causal Discovery from Autocorrelated and Non-stationary Data (Student Abstract)—0
ECGAN: Self-supervised generative adversarial network for electrocardiography—0
ECG synthesis with Neural ODE and GAN models—0
Echo State Network for two-dimensional turbulent moist Rayleigh-Bénard convection—0
Echo State Networks trained by Tikhonov least squares are L2(μ) approximators of ergodic dynamical systems—0
Econometric Analysis of Pandemic Disruption and Recovery Trajectory in the U.S. Rail Freight Industry—0
Econometric Modeling of Regional Electricity Spot Prices in the Australian Market—0
Econometric modelling and forecasting of intraday electricity prices—0
Economic Nowcasting with Long Short-Term Memory Artificial Neural Networks (LSTM)—0
When to Classify Events in Open Times Series?—0
EDGAR: Embedded Detection of Gunshots by AI in Real-time—0
Edges in Brain Networks: Contributions to Models of Structure and Function—0
Edge-Varying Fourier Graph Networks for Multivariate Time Series Forecasting—0
EEG functional connectivity and deep learning for automatic diagnosis of brain disorders: Alzheimer's disease and schizophrenia—0
EEG Signal Dimensionality Reduction and Classification using Tensor Decomposition and Deep Convolutional Neural Networks—0
EEG to fMRI Synthesis: Is Deep Learning a candidate?—0
Effective Bayesian Modeling of Groups of Related Count Time Series—0
Effective Combination of DenseNet andBiLSTM for Keyword Spotting—0
Effective Connectivity-Based Neural Decoding: A Causal Interaction-Driven Approach—0
Effective Split-Merge Monte Carlo Methods for Nonparametric Models of Sequential Data—0
Effect of shapes of activation functions on predictability in the echo state network—0
Effect of temporal resolution on the reproduction of chaotic dynamics via reservoir computing—0
Effects of Correlated Noise on the Performance of Persistence Based Dynamic State Detection Methods—0
Effects of pitch and timing expectancy on musical emotion—0
Effects of weather and policy intervention on COVID-19 infection in Ghana—0
Efficacy the of Confinement Policies on the COVID-19 Spread Dynamics in the Early Period of the Pandemic—0
Efficiency of the Moscow Stock Exchange before 2022—0
Efficient and Consistent Robust Time Series Analysis—0
Show:102550
← PrevPage 128 of 135Next →
All datasetsPhysioNet Challenge 2012Speech CommandsVentilator Pressure Prediction

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1naive classifierF187.47—Unverified
2GRU-D - APC (n = 1)F127.3—Unverified
3GRU-APC (n = 1)F125.7—Unverified
4GRU-DF122.5—Unverified
5GRUF122.3—Unverified
6GRU-SimpleF122.2—Unverified
7GRU-MeanF122.1—Unverified
#ModelMetricClaimedVerifiedStatus
1SepTr% Test Accuracy98.51—Unverified
2ViT% Test Accuracy98.11—Unverified
3FlexTCN-4% Test Accuracy97.73—Unverified
4MatchboxNet% Test Accuracy97.4—Unverified
5CKCNN (100k)% Test Accuracy95.27—Unverified
6FlexTCN-6% Test Accuracy (Raw Data)91.73—Unverified
#ModelMetricClaimedVerifiedStatus
1ResBiLSTMMAE0.13—Unverified