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 3226–3250 of 6748 papers

TitleStatusHype
Integrating Domain Knowledge in Data-driven Earth Observation with Process Convolutions—0
Explorative Data Analysis of Time Series based AlgorithmFeatures of CMA-ES Variants—0
Viking: Variational Bayesian Variance Tracking—0
Demographic-Guided Attention in Recurrent Neural Networks for Modeling Neuropathophysiological Heterogeneity—0
Tracking agitation in people living with dementia in a care environment—0
Process Outcome Prediction: CNN vs. LSTM (with Attention)—0
Forecasting COVID-19 Counts At A Single Hospital: A Hierarchical Bayesian ApproachCode0
Underwater dual-magnification imaging for automated lake plankton monitoring—0
Bayesian Optimisation for a Biologically Inspired Population Neural Network—0
LioNets: A Neural-Specific Local Interpretation Technique Exploiting Penultimate Layer InformationCode0
A Bayesian analysis of gain-loss asymmetry—0
A Fast Evidential Approach for Stock Forecasting—0
Analysis of bank leverage via dynamical systems and deep neural networks—0
Boosted Embeddings for Time Series Forecasting—0
Deep Transformer Networks for Time Series Classification: The NPP Safety Case—0
Granger Causality Based Hierarchical Time Series Clustering for State Estimation—0
Large-scale nonlinear Granger causality for inferring directed dependence from short multivariate time-series data—0
Flow-based Spatio-Temporal Structured Prediction of Motion DynamicsCode0
Neural Network for Weighted Signal Temporal Logic—0
Fingerprint Presentation Attack Detection utilizing Time-Series, Color Fingerprint Captures—0
CARRNN: A Continuous Autoregressive Recurrent Neural Network for Deep Representation Learning from Sporadic Temporal Data—0
Market Regime Detection via Realized Covariances: A Comparison between Unsupervised Learning and Nonlinear Models—0
Evaluation of Time Series Forecasting Models for Estimation of PM2.5 Levels in Air—0
Min(d)ing the President: A text analytic approach to measuring tax news—0
Bootstrap Inference for Hawkes and General Point Processes—0
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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