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
Complex systems approach to natural language—0
A Review on Deep Learning in UAV Remote Sensing—0
Complex market dynamics in the light of random matrix theory—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
Complexity Measures and Features for Times Series classification—0
Indoor Localization Using Smartphone Magnetic with Multi-Scale TCN and LSTM—0
A Review of Wind Speed and Wind Power Forecasting Techniques—0
A Method for Estimating the Entropy of Time Series Using Artificial Neural Networks—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
Forecasting with a Panel Tobit Model—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
Forecasting Using Reservoir Computing: The Role of Generalized Synchronization—0
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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