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 4301–4325 of 6748 papers

TitleStatusHype
Improving the Thermal Infrared Monitoring of Volcanoes: A Deep Learning Approach for Intermittent Image Series—0
Improving Time Series Classification Algorithms Using Octave-Convolutional Layers—0
Structural Inference in Sparse High-Dimensional Vector Autoregressions—0
Forecasting, Causality, and Impulse Response with Neural Vector Autoregressions—0
Imputation of Clinical Covariates in Time Series—0
Imputation of Missing Streamflow Data at Multiple Gauging Stations in Benin Republic—0
Imputing Missing Observations with Time Sliced Synthetic Minority Oversampling Technique—0
IMUOptimize: A Data-Driven Approach to Optimal IMU Placement for Human Pose Estimation with Transformer Architecture—0
Including Sparse Production Knowledge into Variational Autoencoders to Increase Anomaly Detection Reliability—0
Incorporating Taylor Series and Recursive Structure in Neural Networks for Time Series Prediction—0
Increasing Server Availability for Overall System Security: A Preventive Maintenance Approach Based on Failure Prediction—0
Increasing the Interpretability of Recurrent Neural Networks Using Hidden Markov Models—0
Independence clustering (without a matrix)—0
Independent Innovation Analysis for Nonlinear Vector Autoregressive Process—0
Indexing the Event Calculus with Kd-trees to Monitor Diabetes—0
Indian Economy and Nighttime Lights—0
Indirect Measurement of Hepatic Drug Clearance by Fitting Dynamical Models—0
Individualized Time-Series Segmentation for Mining Mobile Phone User Behavior—0
Individual Topology Structure of Eye Movement Trajectories—0
Indoor environment data time-series reconstruction using autoencoder neural networks—0
Indoor Localization Using Smartphone Magnetic with Multi-Scale TCN and LSTM—0
Inductive Granger Causal Modeling for Multivariate Time Series—0
Inductive Predictions of Extreme Hydrologic Events in The Wabash River Watershed—0
Industrial Forecasting with Exponentially Smoothed Recurrent Neural Networks—0
Inference for Network Structure and Dynamics from Time Series Data via Graph Neural Network—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