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 4101–4150 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
Unsupervised Clustering of Time Series Signals using Neuromorphic Energy-Efficient Temporal Neural Networks—0
Outlier detection at the parcel-level in wheat and rapeseed crops using multispectral and SAR time series—0
Unsupervised Deep Anomaly Detection for Multi-Sensor Time-Series Signals—0
Unsupervised Deep Learning for IoT Time Series—0
Unsupervised detection of diachronic word sense evolution—0
Unsupervised Driving Behavior Analysis using Representation Learning and Exploiting Group-based Training—0
Unsupervised Driving Event Discovery Based on Vehicle CAN-data—0
Unsupervised Event Coreference Resolution—0
Unsupervised Flood Detection on SAR Time Series—0
Unsupervised learning for economic risk evaluation in the context of Covid-19 pandemic—0
Unsupervised Learning in Reservoir Computing for EEG-based Emotion Recognition—0
Unsupervised Learning through Temporal Smoothing and Entropy Maximization—0
Unsupervised model-free representation learning—0
Unsupervised non-parametric change point detection in quasi-periodic signals—0
Unsupervised Online Anomaly Detection On Irregularly Sampled Or Missing Valued Time-Series Data Using LSTM Networks—0
Unsupervised Prediction of Negative Health Events Ahead of Time—0
Unsupervised Representation for EHR Signals and Codes as Patient Status Vector—0
Unsupervised Representation Learning and Anomaly Detection in ECG Sequences—0
Unsupervised Time Series Outlier Detection with Diversity-Driven Convolutional Ensembles -- Extended Version—0
Unsupervised Time-Series Representation Learning with Iterative Bilinear Temporal-Spectral Fusion—0
Unsupervised Visual Time-Series Representation Learning and Clustering—0
Untargeted, Targeted and Universal Adversarial Attacks and Defenses on Time Series—0
Exploring Representations and Interventions in Time Series Foundation Models—0
Unveiling Early Warning Signals of Systemic Risks in Banks: A Recurrence Network-Based Approach—0
Unveiling the role of plasticity rules in reservoir computing—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