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 2526–2550 of 6748 papers

TitleStatusHype
Neural Ordinary Differential Equation Model for Evolutionary Subspace Clustering and Its Applications—0
Recovering lost and absent information in temporal networksCode0
A Framework for Imbalanced Time-series ForecastingCode0
Deep learning for temporal data representation in electronic health records: A systematic review of challenges and methodologies—0
Multiple species animal movements: network properties, disease dynamic and the impact of targeted control actionsCode0
Adaptive Inducing Points Selection For Gaussian Processes—0
Improving COVID-19 Forecasting using eXogenous VariablesCode0
High-dimensional Multivariate Time Series Forecasting in IoT Applications using Embedding Non-stationary Fuzzy Time Series—0
Approximation Theory of Convolutional Architectures for Time Series Modelling—0
Predicting the 2020 US Presidential Election with Twitter—0
OnlineSTL: Scaling Time Series Decomposition by 100x—0
Topological Attention for Time Series Forecasting—0
Long-term series forecasting with Query Selector -- efficient model of sparse attentionCode1
Wave-based extreme deep learning based on non-linear time-Floquet entanglement—0
Stock price prediction using BERT and GAN—0
ProfileSR-GAN: A GAN based Super-Resolution Method for Generating High-Resolution Load Profiles—0
A Method for Estimating the Entropy of Time Series Using Artificial Neural Networks—0
STRODE: Stochastic Boundary Ordinary Differential EquationCode1
Time Series Anomaly Detection for Smart Grids: A Survey—0
Online Graph Topology Learning from Matrix-valued Time Series—0
Estimating covariant Lyapunov vectors from data—0
Neural Contextual Anomaly Detection for Time SeriesCode1
Panoptic Segmentation of Satellite Image Time Series with Convolutional Temporal Attention NetworksCode1
A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty QuantificationCode1
Integrating LSTMs and GNNs for COVID-19 ForecastingCode1
Show:102550
← PrevPage 102 of 270Next →
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