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 3251–3275 of 6748 papers

TitleStatusHype
Including Sparse Production Knowledge into Variational Autoencoders to Increase Anomaly Detection Reliability—0
A Time Series Analysis-Based Forecasting Framework for the Indian Healthcare Sector—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
Interpretable Time Series Clustering Using Local Explanations—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
Inference in heavy-tailed non-stationary multivariate time series—0
Inference in mixed causal and noncausal models with generalized Student's t-distributions—0
Inference in Non-stationary High-Dimensional VARs—0
Inference of Binary Regime Models with Jump Discontinuities—0
Inference of Causal Information Flow in Collective Animal Behavior—0
Causal-based Time Series Domain Generalization for Vehicle Intention Prediction—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