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 3151–3175 of 6748 papers

TitleStatusHype
FPTN: Fast Pure Transformer Network for Traffic Flow Forecasting—0
Identifiability of latent-variable and structural-equation models: from linear to nonlinear—0
Fourier-RNNs for Modelling Noisy Physics Data—0
Composition Properties of Inferential Privacy for Time-Series Data—0
Identification of Effective Connectivity Subregions—0
Identification of market trends with string and D2-brane maps—0
Identification of Phase-Locked Loop System From Its Experimental Time Series—0
Identification of Recurrent Patterns in the Activation of Brain Networks—0
Identification robust inference for moments based analysis of linear dynamic panel data models—0
Evaluating the Planning and Operational Resilience of Electrical Distribution Systems with Distributed Energy Resources using Complex Network Theory—0
Identifying Constant and Unique Relations by using Time-Series Text—0
Identifying Cover Songs Using Information-Theoretic Measures of Similarity—0
Foundations of Sequence-to-Sequence Modeling for Time Series—0
Foundation Models for Time Series: A Survey—0
Identifying Grey-box Thermal Models with Bayesian Neural Networks—0
FormerTime: Hierarchical Multi-Scale Representations for Multivariate Time Series Classification—0
Identifying nonlinear dynamical systems via generative recurrent neural networks with applications to fMRI—0
Identifying On-road Scenarios Predictive of ADHD usingDriving Simulator Time Series Data—0
Identifying Pairs in Simulated Bio-Medical Time-Series—0
Identifying Patients at Risk of Major Adverse Cardiovascular Events Using Symbolic Mismatch—0
Identifying Predictive Causal Factors from News Streams—0
Identifying Seizure Onset Zone from the Causal Connectivity Inferred Using Directed Information—0
Composite FORCE learning of chaotic echo state networks for time-series prediction—0
Identifying the module structure of swarms using a new framework of network-based time series clustering—0
A review on outlier/anomaly detection in time series data—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