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 4101–4125 of 6748 papers

TitleStatusHype
Unifying Epidemic Models with Mixtures—0
Unifying Prediction and Explanation in Time-Series Transformers via Shapley-based Pretraining—0
United States FDA drug approvals are persistent and polycyclic: Insights into economic cycles, innovation dynamics, and national policy—0
United States Road Accident Prediction using Random Forest Predictor—0
UnitNorm: Rethinking Normalization for Transformers in Time Series—0
Univariate Long-Term Municipal Water Demand Forecasting—0
Universal abundance fluctuations across microbial communities, tropical forests, and urban populations—0
Universal Adversarial Attack on Deep Learning Based Prognostics—0
Universal Codes from Switching Strategies—0
Universal EEG Encoder for Learning Diverse Intelligent Tasks—0
Universal features of price formation in financial markets: perspectives from Deep Learning—0
Universal Fourier Attack for Time Series—0
Universal hidden monotonic trend estimation with contrastive learning—0
Ordinal analysis of lexical patterns—0
Universality of Bayesian mixture predictors—0
UNIVERSAL MODAL EMBEDDING OF DYNAMICS IN VIDEOS AND ITS APPLICATIONS—0
Universal time-series forecasting with mixture predictors—0
Unleashing The Power of Pre-Trained Language Models for Irregularly Sampled Time Series—0
Unraveling S&P500 stock volatility and networks -- An encoding-and-decoding approach—0
Unraveling the Complexity of Splitting Sequential Data: Tackling Challenges in Video and Time Series Analysis—0
Unsupervised and Semi-supervised Anomaly Detection with LSTM Neural Networks—0
Unsupervised Anomaly Detection in Energy Time Series Data Using Variational Recurrent Autoencoders with Attention—0
Unsupervised Anomaly Detection in Stream Data with Online Evolving Spiking Neural Networks—0
Unsupervised Anomaly Detection in Time-series: An Extensive Evaluation and Analysis of State-of-the-art Methods—0
Unsupervised Change Detection using DRE-CUSUM—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